Crowdsourcing synset relations with Genus-Species-Match

Abstract

Enabling a domain-specific lexical resource is useful for improving the performance of a natural language processing system. However, such resources may be represented in the form of glossaries-terms provided with their sense definitions. Despite the problem of integrating such domain-specific glossaries into more sophisticated general purpose resources like thesuari being highly topical, it is complicated by ambiguity of the individual terms. This paper presents Genus-Species-Match, a crowdsourcing workflow for matching noisy pairs of synsets representing hyponymic/hypernymic relations. The system demonstrates F1 score of 80% on an experiment conducted on an online labor marketplace using the EMERCOM glossary and the Yet Another RussNet sense inventory.