Corpus-based Check-up for Thesaurus

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5773–5779 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 5773 . Abstract In this paper we discuss the usefulness of applying a checking procedure to existing thesauri. The procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities. We applied the procedure to more than 30 thousand words of the Russian wordnet and found some serious errors in word sense description, including inaccurate relationships and missing senses of ambiguous words. 1 Introduction Large thesauri such as Princeton WordNet (Fellbaum, 1998) and wordnets created for other languages (Bond and Foster, 2013) are important instruments for natural language processing. Developing and maintaining such resources is a very expensive and time-consuming procedure. At the same time, contemporary computational systems, which can translate texts with almost human quality (Castilho et al., 2017), cannot automatically create such thesauri from scratch providing a structure somehow similar to resources created by professionals (Camacho- Collados, 2017; Camacho-Collados et al., 2018). But if such a thesaurus exists, the developers should have approaches to maintain and improve it. In previous works, various methods on lexical enrichment of thesauri have been studied (Snow et al., 2006; Navigli and Ponzetto, 2012). But another issue was not practically discussed: how to find mistakes in existing thesaurus descriptions: incorrect relations or missed significant senses of ambiguous words, which were not included accidentally or appeared recently. In fact, it is much more difficult to reveal missed and novel senses or wrong relations, if compared to detect novel words (Frermann and Lapata, 2016; Lau et al., 2014). So it is known that such missed senses are often found during semantic annotation of a corpus and this is an additional problem for such annotation (Snyder and Palmer, 2004; Bond and Wang, 2014). In this paper, we consider an approach that uses embedding models to reveal problems in a thesaurus. Previously, distributional and embedding methods were evaluated in comparison with manual data (Baroni and Lenci, 2011; Panchenko et al., 2015). But we can use them in the opposite way: to utilize embedding- based similarities and try to detect some problems in a thesaurus. We study such similarities for more than 30 thousand words presented in Russian wordnet RuWordNet (Loukachevitch et al., 2018)1. RuWordNet was created on the basis of another Russian thesaurus RuThes in 2016, which was developed as a tool for natural language processing during more than 20 years (Loukachevitch and Dobrov, 2002). Currently, the published version of RuWordNet includes 110 thousand Russian words and expressions. 2 Related Work Word sense induction approaches (Agirre and Soroa, 2007; Navigli, 2009; Lau et al., 2014; Panchenko et al., 2018) try to induce senses of ambiguous words from their contexts in a large corpus. Sometimes such approaches can find new senses not described in any lexical resources. But the results of these methods are rarely intended to 1http://ruwordnet.ru/en/ Corpus-based Check-up for Thesaurus Natalia Loukachevitch Research Computing Center Lomonosov …

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