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Multimodal Fusion with Global and Local Features for Text Classification

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

Text classification is a crucial task in natural language processing. Due to the characteristics of text structure, achieving the best result remains an ongoing challenge. In this paper, we propose an ensemble model which outperforms the state-of-the-art. We first utilize rule-based n-gram approach to extend corpus. Then two different features, global dependencies of word and local semantic feature, are extracted by gated recurrent unit and global average pooling model respectively. In order to take advantage of the complementarity of the global and local features, a decision-level fusion is applied to fuse those different kinds of features. We evaluate the quality of our model on various public datasets, including sentiment analysis, ontology classification and text categorization. Experimental results show that our model can effectively learn representations for language modeling, and achieves the best accuracy of text categorization.

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Notes

  1. 1.

    http://www.imdb.com/interfaces.

  2. 2.

    http://ai.stanford.edu/~amaas/data/sentiment/.

  3. 3.

    http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz.

  4. 4.

    http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html.

  5. 5.

    https://www.yelp.com/dataset_challenge.

  6. 6.

    https://nlp.stanford.edu/projects/glove/.

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Acknowledgements

This work is supported by the Special Funds of the National Natural Science Foundation of China (Grant No. 51227803).

Author information

Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Cheng Xu, Yue Wu & Zongtian Liu

Authors
  1. Cheng Xu
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  2. Yue Wu
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  3. Zongtian Liu
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Corresponding author

Correspondence to Yue Wu .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Xu, C., Wu, Y., Liu, Z. (2017). Multimodal Fusion with Global and Local Features for Text Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

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