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Distributed Document Representation for Document Classification

  • Rumeng Li
  • Hiroyuki Shindo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Support Vector Regression Natural Language Processing Machine Translation Recurrent Neural Network Deep Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Peking UniversityBeijingChina
  2. 2.Nara Institute of Science and TechnologyNaraJapan

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