Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Incremental finite-state parsing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
On the evaluation and comparison of taggers: the effect of noise in testing corpora
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Committee-based decision making in probabilistic partial parsing
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Representation trees and string-tree correspondences
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
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The goal of this article is to present our work about a combination of several syntactic parsers to produce a more robust parser. We have built a platform which allows us to compare syntactic parsers for a given language by splitting their results in elementary pieces, normalizing them, and comparing them with reference results. The same platform is used to combine several parsers to produce a dependency parser that has larger coverage and is more robust than its component parsers. In the future, it should be possible to "compile" the knowledge extracted from several analyzers into an autonomous dependency parser.