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 Czech.
REF adapted a Semi-supervised Structured Conditional Model (SS-SCM) to dependency parsing.
An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing
EMNLP
Computer Science