Pairwise classification and support vector machines
Advances in kernel methods
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Incrementality in deterministic dependency parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Computational Linguistics
Chinese dependency parsing with large scale automatically constructed case structures
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Dependency syntax analysis using grammar induction and a lexical categories precedence system
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Analyzing and integrating dependency parsers
Computational Linguistics
Improving transition-based dependency parsing with buffer transitions
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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In this paper, we present a framework for multi-lingual dependency parsing. Our bottom-up deterministic parser adopts Nivre's algorithm (Nivre, 2004) with a preprocessor. Support Vector Machines (SVMs) are utilized to determine the word dependency attachments. Then, a maximum entropy method (MaxEnt) is used for determining the label of the dependency relation. To improve the performance of the parser, we construct a tagger based on SVMs to find neighboring attachment as a preprocessor. Experimental evaluation shows that the proposed extension improves the parsing accuracy of our base parser in 9 languages. (Hajič et al., 2004; Simov et al., 2005; Simov and Osenova, 2003; Chen et al., 2003; Böhmová et al., 2003; Kromann, 2003; van der Beek et al., 2002; Brants et al., 2002; Kawata and Bartels, 2000; Afonso et al., 2002; Džeroski et al., 2006; Civit and Martí, 2002; Nilsson et al., 2005; Oflazer et al., 2003; Atalay et al., 2003).