are learned topics more useful than subject headings
Topic models, through their ability to automatically learn and assign topics to documents in a collection, have the potential to greatly improve how content is organized and searched in digital libraries. However, much remains to be done to assess the value of topic models in digital library applications. In this work, we present results from a user study, in which participants evaluated the similarity of books clustered using matched topics and Library of Congress Subject Headings (LCSH). Topics outperformed LCSH in 11 cases; LCSH outperformed topics in 4. These results suggest that topics are a viable alternative to LCSH.
topic models clustering user evaluation usability