Journal of Guangxi Normal University(Natural Science Edition) ›› 2011, Vol. 29 ›› Issue (3): 147-150.

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Shallow Parsing Based on CRF and Transformation-basedError-driven Learning

ZHANG Fen1,2, QU Wei-guang1,2, ZHAO Hong-yan1,2, ZHOU Jun-sheng1,2   

  1. 1.School of Computer Science and Technology,Nanjing Normal University,Nanjing Jiangsu 210046,China;
    2.The Research Center of Information Security and Confidentiality Technology of Jiangsu Province,Nanjing Jiangsu 210097,China
  • Received:2011-05-25 Online:2011-08-20 Published:2018-12-03

Abstract: This paper proposes a method for shallow parsing on the basis of CRF and transformation-based error-driven learning.The method is applied to Penn Chinese Treebank and gets a good performance of chunking identification.First,CRF model is used to identify chunks to acquire candidate transformation rules by error-driven learning.Then,an evaluationfunction is used to filter candidate transformation rules.And last,transformation rules are used to revise the chunking results of CRF.The experimental results show that this approach is effective,and outperforms the single CRF-based approachin shallow parsing.Precision,recall and F-values are improved respectively.

Key words: shallow parsing, CRF, transformation-based error-driven learning, transformation rules

CLC Number: 

  • TP391.1
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