@inproceedings{amar-liebeskind-2020-jct,
title = "{JCT} at {S}em{E}val-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection",
author = "Amar, Efrat and
Liebeskind, Chaya",
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-1.9",
doi = "10.18653/v1/2020.semeval-1.9",
pages = "90--97",
abstract = "In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change across time in text corpora of German, English, Latin and Swedish. In particular, we analyze the score distribution of existing models. Then we define a general threshold, adjust it independently to each of the models and measure the models{'} score reliability. Finally, using both the threshold and score reliability, we aggregate the models for the two sub- tasks: binary classification and ranking.",
}
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<abstract>In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change across time in text corpora of German, English, Latin and Swedish. In particular, we analyze the score distribution of existing models. Then we define a general threshold, adjust it independently to each of the models and measure the models’ score reliability. Finally, using both the threshold and score reliability, we aggregate the models for the two sub- tasks: binary classification and ranking.</abstract>
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%0 Conference Proceedings
%T JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection
%A Amar, Efrat
%A Liebeskind, Chaya
%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 amar-liebeskind-2020-jct
%X In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change across time in text corpora of German, English, Latin and Swedish. In particular, we analyze the score distribution of existing models. Then we define a general threshold, adjust it independently to each of the models and measure the models’ score reliability. Finally, using both the threshold and score reliability, we aggregate the models for the two sub- tasks: binary classification and ranking.
%R 10.18653/v1/2020.semeval-1.9
%U https://aclanthology.org/2020.semeval-1.9
%U https://doi.org/10.18653/v1/2020.semeval-1.9
%P 90-97
Markdown (Informal)
[JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection](https://aclanthology.org/2020.semeval-1.9) (Amar & Liebeskind, SemEval 2020)
ACL