SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Dominik Schlechtweg; Barbara McGillivray; Simon Hengchen; Haim Dubossarsky; Nina Tahmasebi

SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, Nina Tahmasebi

Digital Humanities

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.

Original language	English
Title of host publication	Proceedings of the Fourteenth Workshop on Semantic Evaluation
Place of Publication	Barcelona (online)
Publisher	International Committee for Computational Linguistics
Pages	1-23
Number of pages	23
Publication status	Published - 1 Dec 2020

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https://www.aclweb.org/anthology/2020.semeval-1.1

Cite this

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abstract = "Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.",

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SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. / Schlechtweg, Dominik; McGillivray, Barbara; Hengchen, Simon et al.
Proceedings of the Fourteenth Workshop on Semantic Evaluation. Barcelona (online): International Committee for Computational Linguistics, 2020. p. 1-23.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

TY - CHAP

T1 - SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

AU - Schlechtweg, Dominik

AU - McGillivray, Barbara

AU - Hengchen, Simon

AU - Dubossarsky, Haim

AU - Tahmasebi, Nina

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.

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M3 - Conference paper

SP - 1

EP - 23

BT - Proceedings of the Fourteenth Workshop on Semantic Evaluation

PB - International Committee for Computational Linguistics

CY - Barcelona (online)

ER -

SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Abstract

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