Structural Correspondence Learning

Discriminative learning methods are widely used in natural language process- ing. These methods work best when their training and test data are drawn from the same distribution. For many NLP …...

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed dif- ferently in different domains, and annotating corpora for every possible domain of interest …...

Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency pars- ing (McDonald et al., 2005). To induce the correspondences among dependency edges from …...

We present a new approach to cross- language text classification that builds on structural correspondence learning, a re- cently proposed theory for domain adap- tation. The approach uses unlabeled doc- …...

We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes exten- sions to structural correspondence learning needed to make it appropriate for the …...

AbstractWe introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) …...

AbstractDomain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are …...