C4.5: programs for machine learning
C4.5: programs for machine learning
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
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
A statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
In question answering, two heads are better than one
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Graph transformations in data-driven dependency parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Dependency Parsing by Transformation and Combination
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Parser combination by reparsing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Co-parsing with competitive models
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
K-best combination of syntactic parsers
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Semi-supervised dependency parsing using generalized tri-training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
The effect of semi-supervised learning on parsing long distance dependencies in German and Swedish
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Analyzing and integrating dependency parsers
Computational Linguistics
Effective parsing using competing CFG rules
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Combining czech dependency parsers
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Hybrid combination of constituency and dependency trees into an ensemble dependency parser
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
A comparative study of classifier combination applied to NLP tasks
Information Fusion
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This paper explores the possibilities of improving parsing results by combining outputs of several parsers. To some extent, we are porting the ideas of Henderson and Brill (1999) to the world of dependency structures. We differ from them in exploring context features more deeply. All our experiments were conducted on Czech but the method is language-independent. We were able to significantly improve over the best parsing result for the given setting, known so far. Moreover, our experiments show that even parsers far below the state of the art can contribute to the total improvement.