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

Authors:
Lukas Michelbacher;Qi Han;Hinrich Schütze
Affiliations:
Institute for Natural Language Processing, University of Stuttgart, Germany;Institute for Natural Language Processing, University of Stuttgart, Germany;Institute for Natural Language Processing, University of Stuttgart, Germany
Venue:
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Year:
2013

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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.