PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Simple Semi-supervised Dependency Parsing
Terry Koo, Xavier Carreras and Michael Collins
In: ACL 2008, 16-18 June 2008, Columbus, Ohio USA.


We present a simple and effective semisupervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and Prague Dependency Treebank, and we show that the cluster-based features yield substantial gains in performance across a wide range of conditions. For example, in the case of English unlabeled second-order parsing, we improve from a baseline accuracy of 92.02% to 93.16%, and in the case of Czech unlabeled second-order parsing, we improve from a baseline accuracy of 86.13% to 87.13%. In addition, we demonstrate that our method also improves performance when small amounts of training data are available, and can roughly halve the amount of supervised data required to reach a desired level of performance.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:4511
Deposited By:Xavier Carreras
Deposited On:13 March 2009