Word Sense Disambiguation for Russian Verbs Using Semantic Vectors and Dictionary Entries

Компьютерная лингвистика и интеллектуальные технологии. 2016. No. 15. P. 393–405.

Lopukhina A., Лопухин К. А.

Word sense disambiguation (WSD) methods are useful for many NLP tasks that require semantic interpretation of input. Furthermore, such methods can help estimate word sense frequencies in different corpora, which is important for lexicographic studies and language learning resources. Although previous research on Russian polysemous verbs disambiguation established some important and interesting results, it was mostly focused on reducing ambiguity or determining the most frequent sense, but not on evaluating WSD accuracy. To the best of our knowledge, there is no comprehensively evaluated method that can perform semi-supervised word sense disambiguation for Russian verbs. In this paper we present a WSD method for verbs that is able to reach an average disambiguation accuracy of 75% using only available linguistic resources: examples and collocations from the Active Dictionary of Russian and large unlabeled corpora. We evaluate the method on contexts sampled from the web-based corpus RuTenTen11 for 10 verbs with 100 contexts for each verb. We compare different variations of the method and analyze its limitations. Method’s implementation and labeled contexts are available online.

Keywords: polysemy, word2vec, word sense disambiguation, sense frequency, semantic vectors