SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

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


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.
Anthology ID:
2020.semeval-1.1
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1–23
Language:
URL:
https://aclanthology.org/2020.semeval-1.1
DOI:
10.18653/v1/2020.semeval-1.1
Bibkey:
Cite (ACL):
Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi. 2020. SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1–23, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Schlechtweg et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.1.pdf
Code
 additional community code