SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
This work addresses a critical evaluation gap for researchers in NLP, lexicography, and linguistics, enabling progress in detecting word meaning changes over time.
The paper tackled the lack of gold standards in lexical semantic change detection by organizing the first shared task, which provided an evaluation framework and manually annotated datasets for four languages, resulting in 33 teams submitting 186 systems.
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.