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The problem of determining semantic similarity between words affects the understanding of synonymy and creates obstacles to the work oflexico graphers. The study was carried out as a part ofa larger research project on expert assessment of synonymic rows in RuWordNet thesaurus (a WordNet–like thesaurus for the Russian language). The aim of this study is to analyze RuWordNet thesaurus and compare it with classical dictionaries of Russian synonyms. For this purpose, the authors singled out entry words (adjectives N = 68 and verbs N = 117) and their analogues (adjectives N = 558 and verbs N = 1410) from the New Explanatory Dictionary of Russian Synonyms by Yu. Apresyan (NEDS). An analogue is viewed as aword whose meaning essentially intersects with the general meaning of a given synonymic row, although it lacks the needed semantic similarity that could indicate the presence of synonymy or near–synonymy (Apresyan). The quantitative analysis based on the breadth–first search (BFS) algorithm estimated the distance between each pair entry word! analogue. The quantitative method revealed that the analogues described in NEDS correlate with the hyponyms and hyperonyms in RuWordNet which contributes to the study of near–synonymy. The qualitative method (observation and linguistic interpretation) was used to analyze pairs entry word! analogue which showed the longest distance; such words were 52 adjectives and15 verbs. First, the meanings of entry words and analogues were checked against two Russian language thesauri, then, their representation in the tree graph of RuWordNet was traced. The analysis revealed inaccuracies concerning the similarity between certainwords. The recommendations for further improvement of RuWordNet were given.

This article explores the principles of synsets in the RuWordNet thesaurus and synonyms in the classical dictionaries of Russian synonyms (N = 10) to identify discrepancies and improve the principles of organising synsets in RuWordNet. The relevance of the study is determined by the demand for WordNet resources in natural language processing tasks. The authors selected 102 RuWordNet thesaurus synsets, including nouns (N = 34), adjectives (N = 34) and verbs (N = 34). The meanings of the lexemes were correlated according to the data given in Russian language thesauri (N = 2). The comparative method and an independent expert assessment of RuWordNet revealed a number of discrepancies and inaccuracies in the representation of synsets concerning polysemy, hypo-hyperonymic relationships, lexical meanings of words and parts-of-speech synonymy. On the basis of this study, the authors recommend the elimination of individual shortcomings in the construction of the RuWord-Net synsets, in particular the polysemy and parts-of-speech synonymy.