The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
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
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Generative models for statistical parsing with Combinatory Categorial Grammar
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Hierarchical directed acyclic graph kernel: methods for structured natural language data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
An SVM based voting algorithm with application to parse reranking
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Minimally lexicalized dependency parsing
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Head-driven transition-based parsing with top-down prediction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Word dependency is important in parsing technology. Some applications such as Information Extraction from biological documents benefit from word dependency analysis even without phrase labels. Therefore, we expect an accurate dependency analyzer trainable without using phrase labels is useful. Although such an English word dependency analyzer was proposed by Yamada and Matsumoto, its accuracy is lower than state-of-the-art phrase structure parsers because of the lack of top-down information given by phrase labels. This paper shows that the dependency analyzer can be improved by introducing a Root-Node Finder and a Prepositional-Phrase Attachment Resolver. Experimental results show that these modules based on Preference Learning give better scores than Collins' Model 3 parser for these subproblems. We expect this method is also applicable to phrase structure parsers.