Automatic Labelling of Topics with Neural Embeddings

Abstract

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.

arXiv comment11 pages, 3 figures, published in COLING2016

References in corpus (2)

• Distributed Representations of Words and Phrases and their Compositionality
• Distributed Representations of Sentences and Documents

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