BibTeX

@misc{tahmasebi-etal-2019-proceedings-285886,
	title        = {Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, August 2, 2019, Florence, Italy},
	author       = {Tahmasebi, Nina and Borin, Lars and Jatowt, Adam  and Xu, Yang},
	year         = {2019},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA},
	ISBN         = {978-1-950737-31-4},
}

@inProceedings{dubossarsky-etal-2019-time-281304,
	title        = {Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change },
	abstract     = {State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases. },
	booktitle    = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 28 - August 2, 2019 / Anna Korhonen, David Traum, Lluís Màrquez (Editors)},
	author       = { Dubossarsky, Haim and Hengchen, Simon and Tahmasebi, Nina and Schlechtweg, Dominik },
	year         = {2019},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA},
	ISBN         = {978-1-950737-48-2},
}

@inProceedings{tahmasebi-risse-2017-finding-256637,
	title        = {Finding Individual Word Sense Changes and their Delay in Appearance},
	abstract     = {We  present  a  method  for  detecting  word sense  changes  by  utilizing  automatically
induced word senses.  Our method works on  the  level  of  individual  senses  and  allows a word to have  e.g. one stable sense and then add a novel sense that later experiences  change.
Senses  are  grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic)  senses. We  evaluate  on  a testset, present recall and estimates of the time between expected and found change.},
	booktitle    = {Proceedings of Recent Advances in Natural Language Processing 2017. Varna, Bulgaria 2–8 September, 2017},
	editor       = {Galia Angelova and Kalina Bontcheva and Ruslan Mitkov and Ivelina Nikolova and Irina Temnikova},
	author       = {Tahmasebi, Nina and Risse, Thomas},
	year         = {2017},
	ISBN         = {978-954-452-048-9},
}