Word Sense Disambiguation in Monolingual Dictionaries for Building R ussian W ord N et (2016)

Abstract: Russian Language is currently poorly supported with WordNet-like resources. One of the new efforts for building Russian WordNet involves mining the monolingual dictionaries. While most steps of the building process are straightforward, word sense disambiguation (WSD) is a source of problems. Due to limited word context specific WSD mechanism is required for each kind of relations mined. This paper describes the WSD method used for mining hypernym relations. First part of the paper explains the main reasons for choosing monolingual dictionaries as the primary source of information for Russian language WordNet and states some problems faced during the information extraction. The second part defines algorithm used to extract hyponym-hypernym pair. The third part describes the algorithm used for WSD.

## introduction after the development of princeton wordnet @xcite , two main approaches were widely exploited to create wordnet for any given language: dictionary-based concept @xcite and translation-based approach (see for example, turkish wordnet, @xcite . the last one assumes that there is a correlation between synset and hyponym hierarchy in different languages, even in the languages that come from distant families. bilgin et al. employ bilingual dictionaries for building the turkish wordnet using existing wordnets. multilingual resources represent the next stage in wordnet history. eurowordnet, described by @xcite , was build for dutch, italian, spanish, german, french, czech, estonian and english languages. @xcite explain the methods used to create balkanet for bulgarian, greek, romanian, serbian and turkish languages. these projects developed monolingual wordnets for a group of languages and aligned them to the structure of princeton wordnet by the means of inter-lingual-index. several attempts were made to create russian wordnet. @xcite attempted to create russian wordnet from scratch using merge approach: first the authors created the core of the base concepts by combining the most frequent russian words and so-called "core of the national mental lexicon", extracted from the russian word association thesaurus, and then proceeded with linking the structure of russnet to eurowordnet. the result, according to project's site foot_0 , contains more than 5500 synsets, which are not published for general use. group of @xcite started a large project based on bilingual and monolingual dictionaries and manual lexicographer work. as for 2004, the project is reported to have nearly 145 000 synsets @xcite ), but no website is available @xcite . @xcite used direct machine translation without any manual interference or proofreading to create a resource for russian wordnet 2 . project ruthes by @xcite , which differs in structure from the canonical princeton wordnet, is a linguistically motivated ontology and contains 158 000 words and 53 500 concepts at the moment of writing. yarn (yet another russnet) project, described by @xcite , is based on the crowdsourcing approach towards creating wordnetlike machine readable open online thesaurus and contains at the time of writing more than 46 500 synsets and more than 119 500 words, but lacks any type of relation between synsets. this paper describes one step of semiautomated effort towards building russian wordnet. the work is based on the hypothesis that existing monolingual dictionaries are the most reliable resource for creating the core of russian wordnet. due to absence of open machine-readable dictionaries (mrd) for russian language the work involves shallow sectioning of a non machine-readable dictionary (non-mrd). this paper focuses on automatic extraction of hypernyms from russian dictionary over a limited number of article types. experts then evaluate the results manually. ## discussion the low resulting quality of disambiguation seems to be a result of several factors: overall difficulty of the task (inter-annotator agreement is 64%), quality of input dictionaries, quality of used similarity database. we also seem to have missed some important linguistic or systemic features of text as well. notably, the algorithms presented are still generically-applicable and do not use hypernym information. despite the low precision in determining the exact hypernyms, the pipeline produces thematically related chains of words. examples of chains, extracted by prefix serelex algorithm are given below with english translation and comparison to princeton wordnet (here ">>" symbolises is_a relation):  rus. спираль >> кривая >> линия eng.'@xmath0, curved @xmath1. передняя >> комната >> помещение eng. '@xmath2. рост >> высота >> расстояние eng. 'stature, @xmath3, @xmath4, and the dictionary we selected provides us with a huge set of difficulties: abbreviations; alternating language in sense definitions; not all head words are lemmas (e.g. plural for nouns that have singular); poor quality of sectioning in ocr. sectioning within bred presents a large problem due to underspecified vaguely nested nature of sections. properly digitized openly published russian dictionary is really wished for. another problem with the dictionary is presence of nearly-identical definitions for the same term. due to restricted context in dictionary in some cases it is difficult even for a human annotator to guess correctly whether a given pair of definitions describes the same concepts or two very distinct ones. this is especially true with abstract terms like time (rus.: время), but physical entities like field (rus.: поле) also present such troubles. one further step to building the russian wordnet is to differentiate hypernyms from synonyms and co-hyponyms. currently we hope to achieve this through classification of definitions and developing morphosyntactic templates to match different relation types within them. this is out of the scope of the current article though. ## conclusion in this work we present a new pipeline for disambiguating and testing disambiguation frame-works for building wordnet relations from raw dictionary data in russian language 3 . we described new algorithm for hypernym disambiguation which performs somewhat better than baseline in cases where annotators agree. the possibility for better disambiguation of specific relation types within dictionaries to be still open. the resulting network, though noisy, is very suitable for rapid manual filtering.