Improving topic models with Latent Feature Word Representations

Dat Quoc Nguyen, Richard Billingsley, Lan Du, Mark Johnson

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Abstract

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.
Original languageEnglish
Pages (from-to)299-313
Number of pages15
JournalTransactions of the Association for Computational Linguistics
Volume3
Publication statusPublished - 2015

Bibliographical note

Copyright the Publisher 2015. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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