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.
Topics & Concepts
Computer scienceSemEvalNatural language processingTask (project management)Semantic changeArtificial intelligenceGermanLexicographyMeaning (existential)Quality (philosophy)LinguisticsPsychologyEconomicsPsychotherapistEpistemologyPhilosophyManagementAuthorship Attribution and ProfilingLanguage and cultural evolutionTopic Modeling