BibTeX

@incollection{tahmasebi-dubossarsky-2023-computational-325543,
	title        = {Computational modeling of semantic change},
	abstract     = {In this chapter we provide an overview of computational modeling for semantic change using large and semi-large textual corpora. We aim to provide a key for the interpretation of relevant methods and evaluation techniques, and also provide insights into important aspects of the computational study of semantic change. We discuss the pros and cons of different classes of models with respect to the properties of the data from which one wishes to model semantic change, and which avenues are available to evaluate the results. This chapter is forthcoming as the book has not yet been published. },
	booktitle    = {Routledge Handbook of Historical Linguistics, 2nd edition},
	author       = {Tahmasebi, Nina and Dubossarsky, Haim},
	year         = {2023},
	publisher    = {Routledge},
}

@inProceedings{berdicevskis-etal-2023-superlim-331445,
	title        = {Superlim: A Swedish Language Understanding Evaluation Benchmark},
	booktitle    = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, December 6-10, 2023, Singapore  / Houda Bouamor, Juan Pino, Kalika Bali (Editors)},
	author       = {Berdicevskis, Aleksandrs and Bouma, Gerlof and Kurtz, Robin and Morger, Felix and Öhman, Joey and Adesam, Yvonne and Borin, Lars and Dannélls, Dana and Forsberg, Markus and Isbister, Tim and Lindahl, Anna and Malmsten, Martin and Rekathati, Faton and Sahlgren, Magnus and Volodina, Elena and Börjeson, Love and Hengchen, Simon and Tahmasebi, Nina},
	year         = {2023},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA},
	ISBN         = {979-8-89176-060-8},
	pages        = {8137--8153},
}

@inProceedings{zhou-etal-2023-finer-325541,
	title        = {The Finer They Get: Combining Fine-Tuned Models For Better Semantic Change Detection},
	abstract     = {In this work we investigate the hypothesis that enriching contextualized models using fine-tuning tasks can improve their
capacity to detect lexical semantic change (LSC). We include tasks  aimed to capture both low-level linguistic information like part-of-speech tagging, as well as higher level (semantic) information.
 
Through a series of analyses we demonstrate that certain combinations of fine-tuning tasks, like sentiment, syntactic information, and logical inference, bring large improvements to standard LSC models that are based only on standard language modeling. We test on the binary classification and ranking tasks of SemEval-2020 Task 1 and evaluate using both permutation tests and under transfer-learning scenarios.},
	booktitle    = {24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
	author       = {Zhou, Wei and Tahmasebi, Nina and Dubossarsky, Haim},
	year         = {2023},
	publisher    = {Linköping University Electronic Press},
	ISBN         = {978-99-1621-999-7},
}

@inProceedings{ohlsson-etal-2023-going-329710,
	title        = {Going to the market together. A presentation of a
mixed methods project},
	booktitle    = {TwinTalks Workshop at DH2023, 10 July, Graz, Austria},
	author       = {Ohlsson, Claes and Virk, Shafqat and Tahmasebi, Nina},
	year         = {2023},
}

@misc{tahmasebi-etal-2023-proceedings-331093,
	title        = {Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change, LChange'23, December 6th, 2023, Singapore},
	abstract     = {Welcome to the 4th International Workshop on Computational Approaches to Historical Language Change (LChange’23) co-located with EMNLP 2023. LChange is held on December 6th, 2023, as a hybrid
event with participation possible both virtually and on-site in Singapore.

Characterizing the time-varying nature of language will have broad implications and applications in
multiple fields including linguistics, artificial intelligence, digital humanities, computational cognitive
and social sciences. In this workshop, we bring together the world’s pioneers and experts in computational approaches to historical language change with a focus on digital text corpora. In doing so, this workshop carries out the triple goals of disseminating state-of-the-art research on diachronic modeling of language change, fostering cross-disciplinary collaborations, and exploring the fundamental theoretical and methodological challenges in this growing niche of computational linguistic research.},
	author       = {Tahmasebi, Nina and Montariol, Syrielle and Dubossarsky, Haim and Kutuzov, Andrey and Hengchen, Simon and Alfter, David and Periti, Francesco and Cassotti, Pierluigi},
	year         = {2023},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA},
	ISBN         = {979-8-89176-043-1},
}