Comparing Two Thesaurus Representations for R ussian (2018)

Abstract: In the paper we presented a new Russian wordnet, RuWordNet, which was semi-automatically obtained by transformation of the existing Russian thesaurus RuThes. At the first step, the basic structure of wordnets was reproduced: synsets’ hierarchy for each part of speech and the basic set of relations between synsets (hyponym-hypernym, part-whole, antonyms). At the second stage, we added causation, entailment and domain relations between synsets. Also derivation relations were established for single words and the component structure for phrases included in RuWordNet. The described procedure of transformation highlights the specific features of each type of thesaurus representations.

## introduction wordnet thesaurus is one of the popular language resources for natural language processing @xcite . the projects for creating wordnet-like resources have been initiated for many languages in the world @xcite . other thesaurus models are rarely discussed, created and used in nlp. in several works, s.szpakowicz and coauthors @xcite @xcite evaluated two versions of roget's thesaurus in several applications. borin and colleagues @xcite compared the structure of the swedish thesaurus saldo with the wordnet structure. in @xcite automatic generation of swedish roget's thesaurus and its comparing with the existing roget-style thesaurus for swedish is discussed. for the russian language, ruthes thesaurus has been created more than fifteen years ago @xcite . it was utilized in various information-retrieval and nlp applications @xcite . ruthes was successfully evaluated in text summarization @xcite ), text clustering @xcite ), text categorization @xcite , detecting russian paraphrases @xcite , etc. using the ruthes model for the concept representation, several domain-specific thesauri have been created for nlp and domain-specific information-retrieval applications including sociopolitical thesaurus @xcite , ontology on natural sciences and technology @xcite , banking thesaurus @xcite and others. currently, ruthes concepts provide a basis for creating the tatar socio-political thesaurus @xcite . in 2013, ruthes was partially published for non-commercial use @xcite . but people would like to have a large russian wordnet. therefore, we have initiated a transforming procedure from the published version of ruthes (ruthes-lite) to the largest russian wordnet (ruwordnet foot_0 ), which we describe in this paper. this transformation allows us to show similarities and differences between two resources in a detailed way. ruwordnet currently includes 115 thousand unique words and phrases. the structure of this paper is as follows. in section 2, we describe related work. section 3 presents the structure of ruthes thesaurus, including the set of relations and principles of work with multiword expressions. section 4 describes the main stages for creating the basic structure of ruwordnet. section 5 is devoted to enrichment of the basic ruwordnet relations. ## related work creating large lexical resources like wordnet from scratch is a complex task, which requires effort for many years @xcite . to speed up the development of a wordnet for own language, the first version of such a resource can be created by automatically translating princeton wordnet into the target language @xcite @xcite , but then considerable effort is required to proofread and correct the obtained translation. as an intermediate approach, researchers propose a two-stage creation of a wordnet for a new language: first translating and transferring the relations of the top concepts of princeton wordnet (the so-called core wordnet), and then manually replenishing hierarchies based on dictionaries and text corpora. this approach was used in the creation of such resources as dannet @xcite and eurowordnet @xcite . after analyzing the existing approaches to the development of wordnets, the creators of the finnish wordnet (fiwn) decided to translate princeton wordnet manually, using the work of professional translators. as a result, the finnish wordnet was created on the basis of translation of more than 200 thousand word senses of princeton wordnet words within 100 days @xcite . in work @xcite , it was proposed to develop a new russian wordnet (yarn) using the russian wiktionary and crowdsourcing. the authors planned to attract a large number of students and interested people to create a new resource. there are at least four known projects for creating a wordnet for the russian language. in russnet @xcite , the authors planned to create the russian wordnet from scratch, guided by the principles of princeton wordnet. in two different projects described in @xcite , attempts were made to automatically translate wordnet into russian, with all the orig-inal thesaurus structure preserved. the results of @xcite are published, but the analysis of the thesaurus generated in this way shows that it requires considerable editing or the use of better algorithms. the last project yarn (yet another russian wordnet) was initiated in 2012 and initially was created on the basis of crowdsourcing, i.e. participation in the work of filling the thesaurus by a large number of participants. currently, yarn contains a significant number of synsets with a small number of relationships between them. the published version foot_1 of the yarn thesaurus contains too many similar or partially similar synsets. in @xcite , the authors describe the project on the integration of the thesaurus russnet @xcite and the thesaurus yarn @xcite into a single linguistic resource, where the expert approach and the crowdsourcing will be combined. in @xcite , a new approach to automatic wordnet construction is presented and tested on a specially prepared russian dataset comprising senses of 600 words @xcite . the approach is based on translation of english synsets, and a number of techniques of clustering and assessing the obtained translation. for russian, the authors report 60% f-measure on the above-mentioned tests. however, the analysis of the dataset showed that the presented russian words have much more senses than it is usually presented in russian dictionaries. for example, word опасность (danger) is usually described as having 2 senses. but in the dataset it has 6 senses. word оборудование (equipment) is usually described with 2 senses, but in the dataset it has 8 senses. it looks that the expert labeling of russian senses for the dataset was somehow biased to english and its representation in princeton wordnet. ## ruthes structure and relations ruthes @xcite and wordnet are both thesauri, i.e. lexical resources in that words similar in meaning are gathered into synsets (wordnet) or concepts (ruthes), between which relations are established. when applying the two thesauri to text processing, similar steps should be carried out, including a comparison of the text with the thesaurus, and the use of the described relations if necessary. there are also significant differences between the thesauri. firstly, in ruthes there is no division into lexical networks by parts of speech. any part of speech can be associated with the same ruthes concept, if they mean the same (so-called partof-speech synonyms). each thesaurus concept has a unique name. to provide morpho-syntactic information for a word, each ruthes entry has parts of speech labels. the morpho-syntactic representation of a multiword expression contains the syntactical type of the whole group, the head word, parts of speech and lemmatized forms for each component word. therefore, secondly, when establishing relations in ruthes, it is often impossible to apply synonym tests based on the interchangeability of words in different contexts @xcite . instead, tests are used to detect the denotative similarity of word meanings, for example, "if the entity x in different situations can be called w 1 , can it always be called w 2 ", and vice versa. thus, because of the above-mentioned differences (denotative tests, unique names of concepts), ruthes is closer to ontologies on an imaginary scale from lexical resources to formal ontologies than wordnet-like thesauri @xcite . ## creating basic structure of ruwordnet in our opinion, one of the most distinctive features of wordnet-like resources is their division into synset nets according to parts of speech. therefore, all text entries of ruthes-lite 2.0 were subdivided into three parts of speech: nouns (single nouns, noun groups, or preposition groups), verbs (single verbs and verb groups), adjectives (single adjectives and adjective groups). we have obtained 29,297 noun synsets, 12,865 adjective synsets, and 7,636 verb synsets ( this subdivision was based on the morphosyntactic representation of ruthes-lite 2.0 text entries, which was fulfilled semi-automatically. therefore, a small number of mistakes because of particle treatment (verbs or adjectives) or nominalized adjectives can appear. for example, russian phrase любитель подраться (=драчун) (brawler, scrapper) was treated in this procedure as a verb group and was assigned to the verb synsets. currently all found mistakes are corrected. the divided synsets were linked to each other with the relation of part-of-speech synonymy. the hyponym-hypernym relations were established between synsets of the same part of speech. these relations include direct hyponym-hypernym relations from ruthes-lite 2.0. in addition, the transitivity property of hyponymhypernym relations was employed in cases when a specific synset did not contain a specific part of speech but its parent and child had text entries of this part of speech. in such cases, the hypernymy-hyponymy relation was established between the child and the parent of this synset. similar to the current version of princeton wordnet, in ruwordnet class-instance relations are also established. by now, they had been generated semi-automatically for geographical objects. the part-whole relations from ruthes were semi-automatically transferred and corrected according to traditions of wordnet-like resources. now ruwordnet contains 3.5 thousand partwhole relations. the part-whole relations include the following subtypes: adjectives in ruwordnet similarly to german or polish wordnets @xcite @xcite are connected with hyponym-hypernym relations. for example, word цветовой (colored) is linked to such hyponyms as красный (red), синий (blue), зеленый (green), еtc. part of speech hypernyms instance . holonyms possyn. ant ony ms noun 39,155 1863 10,010 18,179 454 verb 10,304 0 0 7,143 20 adj. 16,423 0 0 13,794 456 antonymy relations are conceptual relations in ruwordnet, that means they link synsets, not single lexemes. they are introduced for all parts of speech, mainly for synsets denoting properties and states, for example: the current numbers of basic relations described in ruwordnet are presented in ## enrichment of basic relations of ruwordnet basic relations in the ruwordnet thesaurus were supplemented by several types of relations, including the relations of causation and entailment, the domain relation, the relations of word derivation and the relations between phrases and their components.