Parsing free word order languages in the Paninian framework
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
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
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
A Modular Cascaded Approach to Complete Parsing
IALP '09 Proceedings of the 2009 International Conference on Asian Language Processing
Two methods to incorporate local morphosyntactic features in Hindi dependency parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
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
Identification of conjunct verbs in hindi and its effect on parsing accuracy
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
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The paper describes a data driven dependency parsing approach which uses clausal information of a sentence to improve the parser performance. The clausal information is added automatically during the parsing process. We demonstrate the experiments on Hindi, a language with relatively rich case marking system and free-word-order. All the experiments are done using a modified version of MSTParser. We did all the experiments on the ICON 2009 parsing contest data. We achieved an improvement of 0.87% and 0.77% in unlabeled attachment and labeled attachment accuracies respectively over the baseline parsing accuracies.