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Research on Pattern Representation and Reliability in Semi-Supervised Entity Relation Extraction

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Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9713))

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Abstract

This paper proposes a bootstrapping-based method to extract multiple entity relations. Compared with previous entity relation extraction methods, this method analyzes the syntax and semantics of sentences based on traditional context pattern representation. In this way, the features of keyword with the nearest syntactic dependency, phrase structure distance and semantics are extracted so as to form new semantic patterns. To reduce the noise caused by pattern extension, patterns and instances are adopted to verify their reliability mutually. In addition, by combining the information entropy of patterns, accurate and significant instances are selected. Experimental results show that this method effectively improves the quality of patterns and obtains favorable extraction results.

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Author information

Authors and Affiliations

  1. Shanghai University, Shanghai, China

    Feiyue Ye & Nan Tang

Authors
  1. Feiyue Ye
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  2. Nan Tang
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Corresponding author

Correspondence to Nan Tang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzen University, Shenzhen, China

    Li Li

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Ye, F., Tang, N. (2016). Research on Pattern Representation and Reliability in Semi-Supervised Entity Relation Extraction. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_31

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  • DOI: https://doi.org/10.1007/978-3-319-41009-8_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41008-1

  • Online ISBN: 978-3-319-41009-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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