Cognitive Modeling for Topic Expansion

(Short Paper)

Sumant Kulkarni, Srinath Srinivasa, and Rajeev Arora

Abstract. Expanding a topic into its constituent terms used by a population is an important problem in social space analytics. A topic is defined as a concept representing the semantic “aboutness” of a set of other concepts. What a topic is really “about” may differ across different populations, and discovering this provides sound insights about the population itself. In this paper, we propose an approach for topic expansion, inspired by models from cognitive science – specifically, episodic and semantic memory. We propose an episodic hypothesis that asserts a relationship between a topic and other concepts that are collectively about the topic. The canonical form of this algorithm, while shown to produce good results, does not run in interactive response time. We then propose several simplifications over the canonical form to provide interactive response times without reducing too much on the quality of the results. The proposed simplifications also help in creating topical clusters on repositories where there is not enough representation of different terms for the topic.

Keywords: Topic Expansion, Topic Modeling, Text Mining, Social Space Analytics

LNCS 8185, p. 703 ff.

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