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UBC Theses and Dissertations

OCTVis : ontology-based comparison of topic models Ge, Amon Dongfang


Topic modeling is a natural language processing (NLP) task that statistically identifies topics from a set of texts. Evaluating results from topic modeling is difficult in context and often requires domain experts. To facilitate evaluation of topic model results within communication between NLP researchers and domain experts, we present a visual comparison framework, OCTVis, to explore results from two topic models mapped against a domain ontology. The design of OCTVis is based on detailed and abstracted data and task models. We support high-level topic model comparison by mapping topics onto ontology concepts and incorporating topic alignment visualizations. For in-depth exploration of the dataset, display of per-document topic distributions and buddy plots allow comparison of topics, texts, and shared keywords at the document level. Case studies with medical domain experts using healthcare texts indicate that our framework enhances qualitative evaluation of topic models and provide a clearer understanding of how topic models can be improved.

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Attribution-NonCommercial-NoDerivatives 4.0 International