@inproceedings{karnysheva-schwarz-2020-tue,
title = "{TUE} at {S}em{E}val-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings",
author = "Karnysheva, Anna and
Schwarz, Pia",
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-1.28",
doi = "10.18653/v1/2020.semeval-1.28",
pages = "232--238",
abstract = "This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. Target words of corpora from two different time periods are classified according to their semantic change. The languages covered are English, German, Latin, and Swedish. Our approach involves clustering ELMo embeddings using DBSCAN and K-means. For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change. The results show that this is a valid approach for the classification subtask where we rank 13th out of 33 groups with an accuracy score of 61.2{\%}. For the ranking subtask we score a Spearman{'}s rank-order correlation coefficient of 0.087 which places us on rank 29.",
}
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<abstract>This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. Target words of corpora from two different time periods are classified according to their semantic change. The languages covered are English, German, Latin, and Swedish. Our approach involves clustering ELMo embeddings using DBSCAN and K-means. For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change. The results show that this is a valid approach for the classification subtask where we rank 13th out of 33 groups with an accuracy score of 61.2%. For the ranking subtask we score a Spearman’s rank-order correlation coefficient of 0.087 which places us on rank 29.</abstract>
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%0 Conference Proceedings
%T TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings
%A Karnysheva, Anna
%A Schwarz, Pia
%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 karnysheva-schwarz-2020-tue
%X This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. Target words of corpora from two different time periods are classified according to their semantic change. The languages covered are English, German, Latin, and Swedish. Our approach involves clustering ELMo embeddings using DBSCAN and K-means. For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change. The results show that this is a valid approach for the classification subtask where we rank 13th out of 33 groups with an accuracy score of 61.2%. For the ranking subtask we score a Spearman’s rank-order correlation coefficient of 0.087 which places us on rank 29.
%R 10.18653/v1/2020.semeval-1.28
%U https://aclanthology.org/2020.semeval-1.28
%U https://doi.org/10.18653/v1/2020.semeval-1.28
%P 232-238
Markdown (Informal)
[TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings](https://aclanthology.org/2020.semeval-1.28) (Karnysheva & Schwarz, SemEval 2020)
ACL