%0 Conference Paper %1 beck-2020-diasense %A Beck, Christin %B Proceedings of the Fourteenth Workshop on Semantic Evaluation %C Barcelona (online) %D 2020 %I International Committee for Computational Linguistics %K 2020 d02 from:christinawarren sfbtrr161 %P 50--58 %T DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings %U https://www.aclweb.org/anthology/2020.semeval-1.4 %X This paper describes DiaSense, a system developed for Task 1 `Unsupervised Lexical Semantic Change Detection' of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.

@inproceedings{beck-2020-diasense, abstract = {This paper describes DiaSense, a system developed for Task 1 {`}Unsupervised Lexical Semantic Change Detection{'} of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.}, added-at = {2021-06-28T10:37:22.000+0200}, address = {Barcelona (online)}, author = {Beck, Christin}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/26701d7912a00388a91b5c446d0994cef/sfbtrr161}, booktitle = {Proceedings of the Fourteenth Workshop on Semantic Evaluation}, interhash = {e6bdf8decf0e2f5332c4bd0cad0c5f40}, intrahash = {6701d7912a00388a91b5c446d0994cef}, keywords = {2020 d02 from:christinawarren sfbtrr161}, month = {12}, pages = {50--58}, publisher = {International Committee for Computational Linguistics}, timestamp = {2022-09-28T15:35:36.000+0200}, title = {DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings}, url = {https://www.aclweb.org/anthology/2020.semeval-1.4}, year = 2020 }