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      Combining Thesaurus Knowledge and Probabilistic Topic Models

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          Abstract

          In this paper we present the approach of introducing thesaurus knowledge into probabilistic topic models. The main idea of the approach is based on the assumption that the frequencies of semantically related words and phrases, which are met in the same texts, should be enhanced: this action leads to their larger contribution into topics found in these texts. We have conducted experiments with several thesauri and found that for improving topic models, it is useful to utilize domain-specific knowledge. If a general thesaurus, such as WordNet, is used, the thesaurus-based improvement of topic models can be achieved with excluding hyponymy relations in combined topic models.

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          Topic modeling

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            Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

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              Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval

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                Author and article information

                Journal
                31 July 2017
                Article
                1707.09816
                5a695154-2839-4729-9a9b-d7da6e24f430

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Accepted to AIST-2017 conference (http://aistconf.ru/). The final publication will be available at link.springer.com
                cs.CL

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