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Bi-Directional Recurrent Attentional Topic Model

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Rong PanAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 6

Article No.: 74, Pages 1 - 30

https://doi.org/10.1145/3412371

Published: 28 September 2020 Publication History

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Abstract

In a document, the topic distribution of a sentence depends on both the topics of its neighbored sentences and its own content, and it is usually affected by the topics of the neighbored sentences with different weights. The neighbored sentences of a sentence include the preceding sentences and the subsequent sentences. Meanwhile, it is natural that a document can be treated as a sequence of sentences. Most existing works for Bayesian document modeling do not take these points into consideration. To fill this gap, we propose a bi-Directional Recurrent Attentional Topic Model (bi-RATM) for document embedding. The bi-RATM not only takes advantage of the sequential orders among sentences but also uses the attention mechanism to model the relations among successive sentences. To support to the bi-RATM, we propose a bi-Directional Recurrent Attentional Bayesian Process (bi-RABP) to handle the sequences. Based on the bi-RABP, bi-RATM fully utilizes the bi-directional sequential information of the sentences in a document. Online bi-RATM is proposed to handle large-scale corpus. Experiments on two corpora show that the proposed model outperforms state-of-the-art methods on document modeling and classification.

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Index Terms

Bi-Directional Recurrent Attentional Topic Model
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling
    2. Machine learning approaches
      1. Learning in probabilistic graphical models
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Document topic models

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 6

December 2020

376 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3427188

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Publication History

Published: 28 September 2020

Accepted: 01 July 2020

Revised: 01 February 2020

Received: 01 December 2018

Published in TKDD Volume 14, Issue 6

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