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CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
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CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
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CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
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NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Simple training of dependency parsers via structured boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Strictly lexical dependency parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
EXPLOITING SUBTREES IN AUTO-PARSED DATA TO IMPROVE DEPENDENCY PARSING
Computational Intelligence
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Dependency parsing has become increasingly popular for a surge of interest lately for applications such as machine translation and question answering. Currently, several supervised learning methods can be used for training high-performance dependency parsers if sufficient labeled data are available. However, currently used statistical dependency parsers provide poor results for words separated by long distances. In order to solve this problem, this article presents an effective dependency parsing approach of incorporating short dependency information from unlabeled data. The unlabeled data is automatically parsed by using a deterministic dependency parser, which exhibits a relatively high performance for short dependencies between words. We then train another parser that uses the information on short dependency relations extracted from the output of the first parser. The proposed approach achieves an unlabeled attachment score of 86.52%, an absolute 1.24% improvement over the baseline system on the Chinese Treebank data set. The results indicate that the proposed approach improves the parsing performance for longer distance words.