@inproceedings{beck-2020-diasense,
title = "{D}ia{S}ense at {S}em{E}val-2020 Task 1: Modeling Sense Change via Pre-trained {BERT} Embeddings",
author = "Beck, Christin",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.4",
doi = "10.18653/v1/2020.semeval-1.4",
pages = "50--58",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings
%A Beck, Christin
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F beck-2020-diasense
%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.
%R 10.18653/v1/2020.semeval-1.4
%U https://aclanthology.org/2020.semeval-1.4
%U https://doi.org/10.18653/v1/2020.semeval-1.4
%P 50-58
Markdown (Informal)
[DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings](https://aclanthology.org/2020.semeval-1.4) (Beck, SemEval 2020)
ACL