C4.5: programs for machine learning
C4.5: programs for machine learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A simple pattern-matching algorithm for recovering empty nodes and their antecedents
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Building deep dependency structures with a wide-coverage CCG parser
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Enriching the output of a parser using memory-based learning
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A robust and hybrid deep-linguistic theory applied to large-scale parsing
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
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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).