An information-theory-based feature type analysis for the modelling of statistical parsing
Authors: 
Sui Zhifang, Zhao Jun, 
Dekai WuAuthors Info & Claims
Sui Zhifang, Zhao Jun, Dekai WuAuthors Info & ClaimsPages 472 - 479
Abstract
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.
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Dekai, Wu, Zhao Jun, Sui Zhifang. 1999b. An Information-Theoretic Empirical Analysis of Dependency-Based Feature Types for Word Prediction Models. Proceedings of EMNLP'99, University of Maryland, USA
- An information-theory-based feature type analysis for the modelling of statistical parsing
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Published: 03 October 2000
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