A syntax-based distributional model for discriminating between semantic similarity and association

Авторы: Trofimov I. V., Suleymanova E. A.
Год: 2017
Тип публикации: Статья
Журнал: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2017”
Том: 1
Номер: 16
Страницы: 349—359
Месяц: < May 31--June 3
Аннотация: In recent years, distributional semantics has shown a trend towards a deeper understanding of what semantic relatedness is and what it is composed of. This is attested, in particular, by the emergence of new gold standards like SimLex999, WS-Sim and WS-Rel. Evidence from cognitive psychology suggests that humans distinguish between two basic types of semantic relations: category-based similarity and thematic association. The paper presents a distributional model capable of differentiating between these relations, and a dataset consisting of 500 similar and 500 associated pairs of nouns that can be used for evaluation of such models.