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International Conference on Intelligent Text Processing and Computational Linguistics

CICLing 2013: Computational Linguistics and Intelligent Text Processing pp 1–12Cite as

Unsupervised Feature Adaptation for Cross-Domain NLP with an Application to Compositionality Grading

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7816))

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.

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Author information

Authors and Affiliations

  1. Institute for Natural Language Processing, University of Stuttgart, Germany

    Lukas Michelbacher, Qi Han & Hinrich Schütze

Authors
  1. Lukas Michelbacher
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  2. Qi Han
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  3. Hinrich Schütze
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Mexico D.F., Mexico

    Alexander Gelbukh

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Michelbacher, L., Han, Q., Schütze, H. (2013). Unsupervised Feature Adaptation for Cross-Domain NLP with an Application to Compositionality Grading. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37247-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-37247-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37246-9

  • Online ISBN: 978-3-642-37247-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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