Probabilistic LR-Parsing with Symbolic Postprocessing

  • Tobias Ruland
Chapter
Part of the Artificial Intelligence book series (AI)

Abstract

This article describes a novel approach to probabilistic LR-parsing of spontaneously spoken utterances developed in Verbmobil. It extends the use of context knowledge within the probabilistic model of the parser and improves its output by applying tree transformation rules learned from corpora. The parser was developed for German, English and Japanese and achieves more than 90% Labeled Recall/Precision on parsed Verbmobil utterances.

Keywords

Context Free Grammar Training Corpus Syntactic Analysis Word Lattice Tree Transformation 
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 2000

Authors and Affiliations

  • Tobias Ruland
    • 1
  1. 1.Siemens AGCorporate TechnologyMünchenGermany

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