Inductive Dependency Parsing (Text, Speech and Language Technology)
Inductive Dependency Parsing (Text, Speech and Language Technology)
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Dependency Parsing
EMNLP '08 Proceedings of the Conference on Empirical Methods 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
Insights into non-projectivity in Hindi
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Two stage constraint based hybrid approach to free word order language dependency parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Efficient third-order dependency parsers
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
On the role of morphosyntactic features in Hindi dependency parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Fast and accurate arc filtering for dependency parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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In this paper we show how linguistic knowledge can be incorporated during graph based parsing. We use MSTParser and show that the use of a constraint graph, instead of a complete graph, to extract a spanning tree improves parsing accuracy. A constraint graph is formed by using linguistic knowledge of a constraint based parsing system. Through a series of experiments we formulate the optimal constraint graph that gives us the best accuracy. These experiments show that some of the previous MSTParser errors can be corrected consistently. It also shows the limitations of the proposed approach.