Using Thesaurus Data to Improve Coreference Resolution for R ussian (2019)

Abstract: Semantic information about entities, specifically, how close in meaning two mentions are to each other, can become very useful for the task of co-reference resolution. One of the most well-researched and widely used forms of presenting this information are measures of semantic similarity and semantic relatedness. These metrics are often computed, relying upon the structure of a thesaurus, but it is also possible to use alternative resources. One such source is Wikipedia, which possesses the category structure similar to that of a thesaurus. In this work we describe an attempt to use semantic relatedness measures, calculated on thesaurus and Wikipedia data, to improve the quality of a co-reference resolution system for Russian language. The results show that this is a viable solution and that combining the two sources yields the most gain in quality.

## introduction coreference resolution is a very important part of many natural language processing tasks, and for solving it generally information from several language layers is required. among those, the importance of semantic information, as opposed to more shallow features, e.g. string-based, morphologic or syntactic ones, is sometimes debated (see e.g. @xcite ), but it is nevertheless seen as useful for overcoming the potential plateau of quality, as v. @xcite noted. as far as english language is concerned, various thesauruses are usually used as sources of semantic information, the most popular of them being the wordnet @xcite . another such resource is wikipedia that, while not a thesaurus by itself, is sometimes considered as such due to its structure of categories, connected to each other by the relation of inclusion @xcite . for russian language the room for improvement of coreference resolution systems still exists, as has been demonstrated by results of the ru-eval-2014 competition for russian coreference resolvers @xcite . the usage of semantic information is also not as widespread, partly due to lesser volume of resources available: fewer thesauruses exist for russian than there are for english, the most prominent of them being the ruthes @xcite , consisting of appr. 70 000 synsets, and the russian segment of wikipedia is also smaller. consequently, fewer attempts at using semantic information have been made. nevertheless, the results of @xcite mentioned above clearly show that semantic information needs to be explored to properly resolve cases such as (1) below. the achieved results suggest that integrating features based on semantic information does indeed improve the system performance, with the highest increase in quality being gained by combining the data from both resources. ## related work thesauruses, in particular wordnet, have been widely used for purposes of coreference resolution in a variety of ways. some of these include extracting hypernym chains or semantic classes, derived from high-level nodes @xcite or calculating special confidence measures of different paths between concepts @xcite . semantic similarity has also been frequently employed in automated coreference resolution, either calculated from thesaurus data or unannotated corpora @xcite , or based on word embeddings @xcite . a large spectrum of different semantic similarity values that can be calculated based on thesaurus structure has been suggested by various researchers. overview of the most influential ones are given, e.g., in @xcite . for russian the research of coreference resolution using thesaurus data has been smaller in scale with the only participant system of ru-eval-2014 that used semantic information relying on a proprietary ontology @xcite . recently, @xcite have introduced a coreference resolution system, supplemented with semantic information from hypernym chains extracted from ruthes, achieving certain improvements in quality. our research differs in approach with employing semantic similarity measures instead. the wikipedia data is also often used in systems of coreference resolution, including the stanford parser @xcite . generally, the text content of the page is considered for analysis, with its category structure being used in a similar way to a thesaurus in @xcite . the text information and categories of a page from russian wikipedia have been used by @xcite with a positive result, but the category tree as a whole was not considered. ## conclusions and future work in this paper we described an attempt to improve the quality of coreference resolution for russian by introducing features, based on semantic information, obtained from thesaurus data. for that end, we used the thesaurus of russian ruthes and the russian segment of wikipedia to compute several semantic relatedness measures to be used as features in a coreference resolution system. while the results of evaluation of the system cannot yet be called final, they suggest that the quality of coreference resolution for russian can be improved by using features based on semantic information. it is important to remark that the maximum profit was achieved by combining the features from both sources, with wikipedia also being useful despite its open-source nature and being open to free editions by any user. while recent research relying on neural networks for coreference resolution achieve better results for russian (e.g. @xcite ), the gains of using semantic information observed by us and other researchers allow to assume that such algorithms could benefit from implementing it, as well. future work, inspired by this research, lies in exploring other coreference resolution algorithms and improving the quality of semantic features extraction. the former involves exploring more productive techniques of coreference resolution, in particular, assessing the potential of integrating semantic level information in neural networks. the latter involves employing a wider range of semantic relatedness measures, as well as increasing the efficiency of using wikipediabased information. as an alternative to the online encyclopedia, dbpedia can be used. it possesses clearer structure and labeled relations, which could simplify computing semantic relatedness from its data.