A nonparametric model for online topic discovery with word embeddings

Volume: 504, Pages: 32 - 47
Published: Dec 1, 2019
Abstract
With the explosive growth of short documents generated from streaming textual sources (e.g., Twitter), latent topic discovery has become a critical task for short text stream clustering. However, most online clustering models determine the probability of producing a new topic by manually setting some hyper-parameter/threshold, which becomes barrier to achieve better topic discovery results. Moreover, topics generated by using existing models...
Paper Details
Title
A nonparametric model for online topic discovery with word embeddings
Published Date
Dec 1, 2019
Volume
504
Pages
32 - 47
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