Structural correspondence learning for parse disambiguation

Abstract

The paper presents an application of Structural Correspondence Learning (SCL) (Blitzer et al., 2006) for domain adaptation of a stochastic attribute-value grammar (SAVG). So far, SCL has been applied successfully in NLP for Part-of-Speech tagging and Sentiment Analysis (Blitzer et al., 2006; Blitzer et al., 2007). An attempt was made in the CoNLL 2007 shared task to apply SCL to non-projective dependency parsing (Shimizu and Nakagawa, 2007), however, without any clear conclusions. We report on our exploration of applying SCL to adapt a syntactic disambiguation model and show promising initial results on Wikipedia domains. © 2009 Association for Computational Linguistics.