Russian ColloCation ExtRaCtion BasEd on WoRd EmBEddings

Collocation acquisition is a crucial task in language learning as well as in natural language processing. Semantics-oriented computational approaches to collocations are quite rare, especially on Russian language data, and require an underlying semantic formalism. In this paper we exploit a definition of collocation by I. A. Mel’čuk and colleagues (Iordanskaya, Mel’čuk 2007) and apply the theory of lexical functions to the task of collocation extraction. Distributed word vector models serve as a state-of-the-art computational basis for the tested method. For the first time experiments of such type are conducted on available Russian language data, including Russian National Corpus, SynTagRus and RusVectōrēs project resources. The resulting collocation lists are assessed manually and then evaluated by means of precision and MRR metrics. Final scores are quite promising (reaching 0.9 in precision) and described algorithm improvements yield a considerable performance growth.