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

ACL ID D09-1058
Title An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning ap- proach. We describe an extension of semi- supervised 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 re- lated to dependency parsing: The first ex- tension is to combine SS-SCMs with an- other semi-supervised approach, described in (Koo et al., 2008). The second exten- sion is to apply the approach to second- order parsing models, such as those de- scribed in (Carreras, 2007), using a two- stage semi-supervised learning approach. We demonstrate the effectiveness of our proposed methods on dependency parsing experiments using two widely used...