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Pattern Learning for Chinese Open Information Extraction

  • Yang Li
  • Qingliang Miao
  • Tong Guo
  • Ji Geng
  • Changjian Hu
  • Feiyu Xu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 957)

Abstract

Open Information Extraction systems, such as ReVerb, OLLIE, Clause IE, OpenIE 4.2, Sanford OIE, and PredPatt, have attracted much attention on English OIE. However, few studies have been reported on OIE for languages beyond English. This paper presents a Chinese OIE system PLCOIE to extract binary relation triples and N-ary relation tuples from Chinese documents. Our goal is to learn general patterns that is composed of both dependency parsing roles and parts of speech from large corpus, and the learned patterns are used to extract relation tuples from documents. In addition, this paper alleviates trans-classed word issue and light verb construction issue. PLCOIE can extract binary relation triples as well as N-ary relation tuples, and experiments on four real-world data sets show that the results are more precise than state-of-the-art Chinese OIE systems, which indicate that PLCOIE is feasible and effective.

Keywords

Information extraction Trans-classed word LVC Logistic regression 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yang Li
    • 1
  • Qingliang Miao
    • 1
  • Tong Guo
    • 1
  • Ji Geng
    • 2
  • Changjian Hu
    • 1
  • Feiyu Xu
    • 1
  1. 1.LenovoBeijingChina
  2. 2.UESTCChengduChina

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