Paper: Best Topic Word Selection for Topic Labelling

ACL ID C10-2069
Title Best Topic Word Selection for Topic Labelling
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

This paper presents the novel task of best topic word selection, that is the selection of the topic word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic word, and show that, in combination as inputs to a reranking model, we are able to consis- tently achieve results above the baseline of simply selecting the highest-ranked topic word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, us- ing only labellings from independent topic models learned over document collections from different domains and genres.