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Finding non-local dependencies: beyond pattern matching

Published: 07 July 2003 Publication History

Abstract

We describe an algorithm for recovering non-local dependencies in syntactic dependency structures. The pattern-matching approach proposed by Johnson (2002) for a similar task for phrase structure trees is extended with machine learning techniques. The algorithm is essentially a classifier that predicts a non-local dependency given a connected fragment of a dependency structure and a set of structural features for this fragment. Evaluating the algorithm on the Penn Treebank shows an improvement of both precision and recall, compared to the results presented in (Johnson, 2002).

References

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Mark Johnson. 2002. A simple pattern-matching algorithm for recovering empty nodes and their antecedents. In Proceedings of the 40th Meeting of the ACL.
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Jason M. Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In Proceedings of the 16th International Conference on Computational Linguistics (COLING), pages 340--345.
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Michael Collins. 1999. Head-Driven Statistical Models For Natural Language Parsing. Ph.D. thesis, University of Pennsylvania.
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Sabine Buchholz. 2002. Memory-based grammatical relation finding. Ph.D. thesis, Tilburg University.
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Walter Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den Bosch. 2002. TiMBL: Tilburg Memory Based Learner, version 4.3, Reference Guide. ILK Technical Report 02-10, Available from http://ilk.kub.nl/downloads/pub/papers/ilk0210.ps.gz
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J. Ross Quinlan. 1993. C4.5: Programs for machine learning. Morgan Kaufmann Publishers.
[7]
Michael P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313--330.
[8]
Ted Briscoe, John Carroll, Jonathan Graham and Ann Copestake. 2002. Relational evaluation schemes. In Proceedings of the Beyond PARSEVAL Workshop at LREC 2002, pages 4--8.
[9]
Stephen Clark, Julia Hockenmaier, and Mark Steedman. 2002. Building deep dependency structures using a wide-coverage CCG parser. In Proceedings of the 40th Meeting of the ACL, pages 327--334.

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ACL '03: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
July 2003
200 pages
ISBN:0111456789

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Association for Computational Linguistics

United States

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Published: 07 July 2003

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