The Journal of Machine Learning Research
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Dependency treelet translation: syntactically informed phrasal SMT
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Strictly lexical dependency parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Improved Monolingual Hypothesis Alignment for Machine Translation System Combination
ACM Transactions on Asian Language Information Processing (TALIP)
Minimally lexicalized dependency parsing
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Dependency parsing with reference to Slovene, Spanish and Swedish
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Perceptron training for a wide-coverage lexicalized-grammar parser
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
The impact of parse quality on syntactically-informed statistical machine translation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Simple training of dependency parsers via structured boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Improving dependency parsing with subtrees from auto-parsed data
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Features for detecting hedge cues
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Improving graph-based dependency parsing with decision history
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Exploiting web-derived selectional preference to improve statistical dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Dependency parsing schemata and mildly non-projective dependency parsing
Computational Linguistics
EXPLOITING SUBTREES IN AUTO-PARSED DATA TO IMPROVE DEPENDENCY PARSING
Computational Intelligence
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We develop dependency parsers for Arabic, English, Chinese, and Czech using Bayes Point Machines, a training algorithm which is as easy to implement as the perceptron yet competitive with large margin methods. We achieve results comparable to state-of-the-art in English and Czech, and report the first directed dependency parsing accuracies for Arabic and Chinese. Given the multilingual nature of our experiments, we discuss some issues regarding the comparison of dependency parsers for different languages.