Learning parse and translation decisions from examples with rich context

Authors:
Ulf Hermjakob;Raymond J. Mooney
Affiliations:
University of Texas at Austin, Austin, TX;University of Texas at Austin, Austin, TX
Venue:
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Year:
1997

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Abstract

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.