BibTeX

@inProceedings{wiren-etal-2018-svala-285624,
	title        = {SVALA: Annotation of Second-Language Learner Text Based on Mostly Automatic Alignment of Parallel Corpora},
	abstract     = {Annotation of second-language learner text is a cumbersome manual task which in turn requires interpretation to postulate the intended meaning of the learner’s language. This paper describes SVALA, a tool which separates the logical steps in this process while providing rich visual support for each of them. The first step is to pseudonymize the learner text to fulfil the legal and ethical requirements for a distributable learner corpus. The second step is to correct the text, which is carried out in the simplest possible way by text editing. During the editing, SVALA automatically maintains a parallel corpus with alignments between words in the learner source text and corrected text, while the annotator may repair inconsistent word alignments. Finally, the actual labelling of the corrections (the postulated errors) is performed. We describe the objectives, design and workflow of SVALA, and our plans for further development.
},
	booktitle    = {Selected papers from the CLARIN Annual Conference 2018, Pisa, 8-10 October 2018},
	editor       = {Inguna Skadina and Maria Eskevich},
	author       = {Wirén, Mats and Matsson, Arild and Rosén, Dan and Volodina, Elena},
	year         = {2018},
	publisher    = {Linköping University Electronic Press, Linköpings universitet},
	address      = {Linköpings universitet},
	ISBN         = {978-91-7685-034-3},
}

@inProceedings{rosen-etal-2018-error-275363,
	title        = {Error Coding of Second-Language Learner Texts Based on Mostly Automatic Alignment of Parallel Corpora. },
	abstract     = {Error coding of second-language learner text, that is, detecting, correcting and annotating errors, is a cumbersome task which in turn requires interpretation of the text to decide what the errors are. This paper describes a system with which the annotator corrects the learner text by editing it prior to the actual error annotation. During the editing, the system automatically generates a parallel corpus of the learner and corrected texts. Based on this, the work of the annotator consists of three independent tasks that are otherwise often conflated: correcting the learner text, repairing inconsistent alignments, and performing the actual error annotation.},
	booktitle    = {Proceedings of CLARIN-2018 conference,  8-10 October 2018, Pisa, Italy},
	author       = {Rosén, Dan and Wirén, Mats  and Volodina, Elena},
	year         = {2018},
}

@inProceedings{volodina-etal-2018-annotation-275361,
	title        = {Annotation of learner corpora: first SweLL insights.},
	abstract     = {This is a concise description of experiences with learner corpus annotation performed within SweLL project. Experiences include work with legal issues, anonymization, error annotation, normalization and questions relating to quality of annotation. },
	booktitle    = {Proceedings of SLTC 2018, Stockholm, October 7-9, 2018},
	author       = {Volodina, Elena and Granstedt, Lena and Megyesi, Beáta and Prentice, Julia and Rosén, Dan and Schenström, Carl-Johan and Sundberg, Gunlög  and Wirén, Mats},
	year         = {2018},
}

@inProceedings{megyesi-etal-2018-learner-275359,
	title        = {Learner Corpus Anonymization in the Age of GDPR: Insights from the Creation of a Learner Corpus of Swedish},
	abstract     = {This paper reports on the status of learner corpus anonymization for the ongoing research infrastructure project SweLL. The main project aim is to deliver and make available for research a well-annotated corpus of essays written by second language (L2) learners of Swedish. As the practice shows, annotation of learner texts is a sensitive process demanding a lot of compromises between ethical and legal demands on the one hand, and research and technical demands, on the other. Below, is a concise description of the current status of pseudonymization of language learner data to ensure anonymity of the learners, with numerous examples of the above-mentioned compromises.},
	booktitle    = {Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018) at SLTC, Stockholm, 7th November 2018},
	editor       = {Ildikó Pilán and Elena Volodina and David Alfter and Lars Borin},
	author       = {Megyesi, Beata and Granstedt, Lena and  Johansson, Sofia and Prentice, Julia and Rosén, Dan and Schenström, Carl-Johan and Sundberg, Gunlög and  Wirén , Mats and Volodina, Elena},
	year         = {2018},
	publisher    = {Linköping University Electronic Press},
	address      = {Linköpings universitet},
	ISBN         = {978-91-7685-173-9},
}

@inProceedings{malm-etal-2018-uneek-267351,
	title        = {Uneek: a Web Tool for Comparative Analysis of Annotated Texts},
	abstract     = {In this paper, we present Uneek, a web based linguistic tool that performs set operations on raw or annotated texts. The tool may be used for automatic distributional analysis, and for disambiguating polysemy with a method that we refer to as semi-automatic uniqueness differentiation (SUDi). Uneek outputs the intersection and differences between their listed attributes, e.g. POS, dependencies, word forms, frame elements. This makes it an ideal supplement to methods for lumping or splitting in frame development processes. In order to make some of Uneek’s functions more clear, we employ SUDi on a small data set containing the polysemous verb "bake". As of now, Uneek may only run two files at a time, but there are plans to develop the tool so that it may simultaneously operate on multiple files. Finally, we relate the developmental plans for added functionality, to how such functions may support FrameNet work in the future.},
	booktitle    = {Proceedings of the LREC 2018 Workshop International FrameNetWorkshop 2018: Multilingual Framenets and Constructicons, 7-12 May 2018, Miyazaki (Japan)  / [ed] Tiago Timponi Torrent, Lars Borin & Collin F. Baker, 2018},
	author       = {Malm, Per and Ahlberg, Malin and Rosén, Dan},
	year         = {2018},
	ISBN         = {979-10-95546-04-7},
}