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Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tags

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Natural Language Processing and Chinese Computing (NLPCC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10619))

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Abstract

The document-based question answering is to select the answer from a set of candidate sentence for a given question. Most Existing works focus on the sentence-pair modeling, but ignore the peculiars of question-answer pairs. This paper proposes to model the interaction between question words and POS tags, as a special kind of information that is peculiar to question-answer pairs. Such information is integrated into a neural model for answer selection. Experimental results on DBQA Task have shown that our model has achieved better results, compared with several state-of-the-art systems. In addition, it also achieves the best result on NLPCC 2017 Shared Task on DBQA.

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Notes

  1. 1.

    http://code.google.com/archive/p/word2vec.

  2. 2.

    https://github.com/HIT-SCIR/pyltp.

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Acknowledgments

This work is partially supported by National High-Tech R&D Program of China (863 Program) (No. 2015AA015404), and Science and Technology Commission of Shanghai Municipality (No. 14511106802). We are grateful to the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, China

    Zhipeng Xie

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  1. Zhipeng Xie
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Correspondence to Zhipeng Xie .

Editor information

Editors and Affiliations

  1. Fudan University, Shanghai, China

    Xuanjing Huang

  2. Singapore Management University, Singapore, Singapore

    Jing Jiang

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

  5. Soochow University, Suzhou, China

    Yu Hong

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Xie, Z. (2018). Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tags. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-73618-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73617-4

  • Online ISBN: 978-3-319-73618-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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