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        <title>Improving Topic Models with Latent Feature Word Representations</title>
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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.</abstract>
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%0 Journal Article
%T Improving Topic Models with Latent Feature Word Representations
%A Nguyen, Dat Quoc
%A Billingsley, Richard
%A Du, Lan
%A Johnson, Mark
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F nguyen-etal-2015-improving-topic
%X 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.
%R 10.1162/tacl_a_00140
%U https://aclanthology.org/Q15-1022
%U https://doi.org/10.1162/tacl_a_00140
%P 299-313

Markdown (Informal)

[Improving Topic Models with Latent Feature Word Representations](https://aclanthology.org/Q15-1022) (Nguyen et al., TACL 2015)

• Improving Topic Models with Latent Feature Word Representations (Nguyen et al., TACL 2015)

ACL

• Dat Quoc Nguyen, Richard Billingsley, Lan Du, and Mark Johnson. 2015. Improving Topic Models with Latent Feature Word Representations. Transactions of the Association for Computational Linguistics, 3:299–313.