Natalia V. Loukachevitch

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This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for(More)
In this paper we consider a new approach for domain-specific sentiment lexicon extraction in Russian. We propose a set of statistical features and algorithm combination that can discriminate sentiment words in a specific domain. The extraction model is trained in the movie domain and then utilized to other domains. We evaluate the quality of obtained(More)
In the paper we present description of Thesaurus on Sociopolitical life, which was constructed as a tool for automatic text processing of large text collections. Specific features of the thesaurus in comparison to conventional information-retrieval thesauri for manual indexing are described. Evaluation of thesaurus-based information retrieval for short(More)
In the paper we describe development, means of evaluation and applications of Russian–English Sociopolitical Thesaurus specially developed as a linguistic resource for automatic text processing applications. The Sociopolitical domain is not a domain of social research but a broad domain of social relations including economic, political, military, cultural,(More)
The paper describes the method of extraction of two-word domain terms combining their features. The features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statistics of global search engines, and a domain-specific thesaurus. The evaluation of the approach is based on manually created thesauri. We show(More)