Automatic Topic Labeling Using Ontology-Based Topic Models

Abstract

Topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. Topic labeling, which aims to assign meaningful labels for discovered topics, has recently gained significant attention. In this paper, we argue that the quality of topic labeling can be improved by considering ontology concepts rather than words alone, in contrast to previous works in this area, which usually represent topics via groups of words selected from topics. We have created: (1) a topic model that integrates ontological concepts with topic models in a single framework, where each topic and each concept are represented as a multinomial distribution over concepts and over words, respectively, and (2) a topic labeling method based on the ontological meaning of the concepts included in the discovered topics. In selecting the best topic labels, we rely on the semantic relatedness of the concepts and their ontological classifications. The results of our experiments conducted on two different data sets show that introducing concepts as additional, richer features between topics and words and describing topics in terms of concepts offers an effective method for generating meaningful labels for the discovered topics.