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EMNLP
2009

An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing

8 years 11 months ago
An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing
This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semisupervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions related to dependency parsing: The first extension is to combine SS-SCMs with another semi-supervised approach, described in (Koo et al., 2008). The second extension is to apply the approach to secondorder parsing models, such as those described in (Carreras, 2007), using a twostage semi-supervised learning approach. We demonstrate the effectiveness of our proposed methods on dependency parsing experiments using two widely used test collections: the Penn Treebank for English, and the Prague Dependency Treebank for Czech. Our best results on test data in the above datasets achieve 93.79% parent-prediction accuracy for English, and 88.05% for...
Jun Suzuki, Hideki Isozaki, Xavier Carreras, Micha
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EMNLP
Authors Jun Suzuki, Hideki Isozaki, Xavier Carreras, Michael Collins
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