Unsupervised feature adaptation for cross-domain NLP with an application to compositionality grading

Abstract

In this paper, we introduce feature adaptation, an unsupervised method for cross-domain natural language processing (NLP). Feature adaptation adapts a supervised NLP system to a new domain by recomputing feature values while retaining the model and the feature definitions used on the original domain. We demonstrate the effectiveness of feature adaptation through cross-domain experiments in compositionality grading and show that it rivals supervised target domain systems when moving from generic web text to a specialized physics text domain. © 2013 Springer-Verlag.