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Inducing Head-Driven PCFGs with Latent Heads: Refining a Tree-Bank Grammar for Parsing

  • Detlef Prescher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)

Abstract

Although state-of-the-art parsers for natural language are lexicalized, it was recently shown that an accurate unlexicalized parser for the Penn tree-bank can be simply read off a manually refined tree-bank. While lexicalized parsers often suffer from sparse data, manual mark-up is costly and largely based on individual linguistic intuition. Thus, across domains, languages, and tree-bank annotations, a fundamental question arises: Is it possible to automatically induce an accurate parser from a tree-bank without resorting to full lexicalization? In this paper, we show how to induce a probabilistic parser with latent head information from simple linguistic principles. Our parser has a performance of 85.1% (LP/LR F1), which is as good as that of early lexicalized ones. This is remarkable since the induction of probabilistic grammars is in general a hard task.

Keywords

Training Corpus Latent Head Input Tree Auxiliary Node Statistical Parser 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Detlef Prescher
    • 1
  1. 1.Institute for Logic, Language and ComputationUniversity of Amsterdam 

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