Automatic Labelling of Topics with Neural Embeddings
Shraey Bhatia, Jey Han Lau, Timothy J. Baldwin · arXiv (Cornell University) · 2016
Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Compared to a state-of-the-art topic labelling system, our methodology is simpler, more efficient, and finds better topic labels.