Abstract
In this paper, we study thesaurus-based topic models and evaluate them from the point of view of topic coherence. Thesaurus-based topic models enhance the scores of related terms found in the same text, which means that the model encourages these terms to be on the same topics. We evaluate various variants of such models. First, we carry out a manual evaluation of the obtained topics. Second, we study the possibility to use the collected manual data for evaluating new variants of thesaurus-based models, propose a method and select the best its parameters in cross-validation. Third, we apply the created evaluation method to estimate the influence of word frequencies on adding thesaurus relations for generating coherent topic models.
The work is supported by the Russian Foundation for Basic Research (project 16-29-09606).
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Loukachevitch, N., Ivanov, K. (2018). Evaluating Thesaurus-Based Topic Models. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_38
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