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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2015: Advances in Knowledge Discovery and Data Mining pp 212–225Cite as

Distributed Document Representation for Document Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9077))

Abstract

The distributed vector representations learned from the deep learning framework have shown its great power in capturing the semantic meaning of words, phrases and sentences, from which multiple NLP applications have benefited. As words combine to form the meaning of sentences, so do sentences combine to form the meaning of documents, the idea of representing each document with a dense distributed representation holds promise. In this paper, we propose a supervised framework (Compound RNN) for document classification based on document-level distributed representations learned from deep learning architecture. Our framework first obtains the distributed representation at sentence-level by operating on the parse tree structure from recursive neural network, and then obtains the document presentation-level by convoluting the sentence vectors from a recurrent neural network. Our framework (Compound RNN) outperforms existing document representations such as bag-of-words, LDA in multiple text classification/regression tasks.

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Author information

Authors and Affiliations

  1. Peking University, Beijing, 100871, China

    Rumeng Li

  2. Nara Institute of Science and Technology, Nara, 630-0192, Japan

    Hiroyuki Shindo

Authors
  1. Rumeng Li
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  2. Hiroyuki Shindo
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Correspondence to Rumeng Li .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tru Cao

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  4. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu-Bao Ho

  5. University of Hong Kong, Hong Kong, Hong Kong SAR

    David Cheung

  6. Osaka University, Osaka, Japan

    Hiroshi Motoda

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© 2015 Springer International Publishing Switzerland

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Li, R., Shindo, H. (2015). Distributed Document Representation for Document Classification. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-18038-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18037-3

  • Online ISBN: 978-3-319-18038-0

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