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A Knowledge-Based Topic Modeling Approach For Automatic Topic Labeling, Mehdi Allahyari, Seyedamin Pouriyeh, Krys Kochut, Hamid Reza Arabnia

Department of Computer Science Faculty Publications

Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines. Automatic Topic Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality …