Memory-Based Dependency Parsing
نویسندگان
چکیده
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.
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