Improving Topic Coherence with Latent Feature Word Representations in MAP Estimation for Topic Modeling
نویسندگان
چکیده
Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature word vectors have been used to obtain high performance in many natural language processing (NLP) tasks. In this paper, we present a new approach by incorporating word vectors to directly optimize the maximum a posteriori (MAP) estimation in a topic model. Preliminary results show that the word vectors induced from the experimental corpus can be used to improve the assignments of topics to words.
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تاریخ انتشار 2015