Title: Four Keys to Topic Interpretability in Topic Modeling
Abstract: Interpretability of topics built by topic modeling is an important issue for researchers applying this technique. We suggest a new interpretability score, which we select from an interpretability score parametric space defined by four components: a splitting method, a probability estimation method, a confirmation measure and an aggregation function. We designed a regularizer for topic modeling representing this score. The resulting topic modeling method shows significant superiority to all analogs in reflecting human assessments of topic interpretability.
Publication Year: 2018
Publication Date: 2018-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 10
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