@inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, } @inProceedings{Alfter-David2018-276407, title = {SB@ GU at the Complex Word Identification 2018 Shared Task}, booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018}, author = {Alfter, David and Pilán, Ildikó}, year = {2018}, publisher = {Association for Computational Linguistics}, adress = {Stroudsburg, PA, USA}, ISBN = {978-1-948087-11-7}, } @inProceedings{Pilán-Ildikó2018-275367, title = {Investigating the importance of linguistic complexity features across different datasets related to language learning.}, abstract = {We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions. }, booktitle = {Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computational Linguistics }, ISBN = {978-1-948087-62-9}, } @inProceedings{Pilán-Ildikó2018-275366, title = {Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.}, abstract = {The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors. }, booktitle = {Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.}, author = {Pilán, Ildikó and Volodina, Elena}, year = {2018}, publisher = {Association of Computation Linguistics }, ISBN = {978-1-948087-61-2}, } @inProceedings{Alfter-David2018-275364, title = {From Language Learning Platform to Infrastructure for Research on Language Learning}, abstract = {Lärka is an Intelligent Computer-Assisted Language Learning (ICALL) platform developed at Språkbanken, as a flexible and a valuable source of additional learning material (e.g. via corpus- based exercises) and a support tool for both teachers and L2 learners of Swedish and students of (Swedish) linguistics. Nowadays, Lärka is being adapted into a central building block in an emerging second language research infrastructure within a larger context of the text-based research infrastructure developed by the national Swedish Language bank, Språkbanken, and SWE-CLARIN.}, booktitle = {Proceedings of CLARIN-2018 conference, Pisa, Italy}, author = {Alfter, David and Borin, Lars and Pilán, Ildikó and Lindström Tiedemann, Therese and Volodina, Elena}, year = {2018}, } @misc{Pilán-Ildikó2018-275358, title = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018), SLTC, Stockholm, 7th November 2018 }, abstract = {The primary goal of the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL) is to create a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promoting the development of “Computational SLA” through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competencies from these areas for sharing experiences and brainstorming around the future of the field.}, author = {Pilán, Ildikó and Volodina, Elena and Alfter, David and Borin, Lars}, year = {2018}, publisher = {Linköping University Electronic Press}, adress = {Linköpings universitet}, ISBN = {978-91-7685-173-9}, } @article{Kosem-Iztok2018-275354, title = {The image of the monolingual dictionary across Europe. Results of the European survey of dictionary use and culture}, abstract = {The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section}, author = {Kosem, Iztok and Lew, Robert and Müller-Spitzer, Carolin and Ribeiro Silveira, Maria and Wolfer , Sascha and Volodina, Elena and Pilán, Ildikó and Sköldberg, Emma and Holmer, Louise and Dorn, Amelie and Gurrutxaga, Antton and Lorentzen, Henrik and Kallas, Jelena and Abel, Andrea and Tiberius, Carole and Partners , Local}, year = {2018}, volume = {ecy022}, pages = {1--23}, }