This paper reports the results of experiments using memory-based learning to guide a deterministic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, memory-based classifiers for predicting the next action of the parser are constructed. The accuracy of a classifier as such is evaluated on held-out data derived from the treebank, and its performance as a parser guide is evaluated by parsing the held-out portion of the treebank. The evaluation shows that memory-based learning gives a signficant improvement over a previous probabilistic model based on maximum conditional likelihood estimation and that the inclusion of lexical features improves the accuracy even further.
CITATION STYLE
Nivre, J., Hall, J., & Nilsson, J. (2004). Memory-based dependency parsing. In Proceedings of the 8th Conference on Computational Natural Language Learning, CoNLL 2004 - Held in cooperation with HLT-NAACL 2004. Association for Computational Linguistics (ACL).
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