Paper: An Information-Theory-Based Feature Type Analysis For The Modeling Of Statistical Parsing

ACL ID P00-1060
Title An Information-Theory-Based Feature Type Analysis For The Modeling Of Statistical Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2000
Authors
  • Zhifang Sui (University of Science and Technology, Clear Water Bay Hong Kong; Peking University, Beijing China)
  • Zhao Jun (University of Science and Technology, Clear Water Bay Hong Kong)
  • Dekai Wu

The paper proposes an information-theory- based method for feature types analysis in probabilistic evaluation modelling for statistical parsing. The basic idea is that we use entropy and conditional entropy to measure whether a feature type grasps some of the information for syntactic structure prediction. Our experiment quantitatively analyzes several feature types’ power for syntactic structure prediction and draws a series of interesting conclusions.