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Target Detection and Knowledge Learning for Domain Restricted Question Answering

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Book cover Natural Language Processing and Chinese Computing (NLPCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9362))

Abstract

Frequent Asked Questions(FAQ) answering in restricted domain has attracted increasing attentions in various areas. FAQ is a task to automated response user’s typical questions within specific domain. Most researches use NLP parser to analyze user’s intention and employ ontology to enrich the domain knowledge. However, syntax analysis performs poorly on the short and informal FAQ questions, and external ontology knowledge bases in specific domains are usually unavailable and expensive to manually construct. In our research, we propose a semi-automatic domain-restricted FAQ answering framework SDFA, without relying on any external resources. SDFA detects the targets of questions to assist both the fast domain knowledge learning and the answer retrieval. The proposed framework has been successfully applied in real project on bank domain. Extensive experiments on two large datasets demonstrate the effectiveness and efficiency of the approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Mengdi Zhang, Tao Huang, Yixin Cao & Lei Hou

Authors
  1. Mengdi Zhang
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  2. Tao Huang
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  3. Yixin Cao
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  4. Lei Hou
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Corresponding author

Correspondence to Mengdi Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Heng Ji

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

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Zhang, M., Huang, T., Cao, Y., Hou, L. (2015). Target Detection and Knowledge Learning for Domain Restricted Question Answering. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_27

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  • DOI: https://doi.org/10.1007/978-3-319-25207-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25206-3

  • Online ISBN: 978-3-319-25207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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