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A comparative study of pivot selection strategies for unsupervised cross-domain sentiment classification

Published online by Cambridge University Press:  27 June 2018

Xia Cui Open the ORCID record for Xia Cui [Opens in a new window] ,
Noor Al-Bazzaz ,
Danushka Bollegala  and
Frans Coenen
Xia Cui
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: [email protected], [email protected], [email protected], [email protected]
Noor Al-Bazzaz
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: [email protected], [email protected], [email protected], [email protected]
Danushka Bollegala
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: [email protected], [email protected], [email protected], [email protected]
Frans Coenen
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: [email protected], [email protected], [email protected], [email protected]
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Abstract

Selecting pivot features that connect a source domain to a target domain is an important first step in unsupervised domain adaptation (UDA). Although different strategies such as the frequency of a feature in a domain, mutual (or pointwise mutual) information have been proposed in prior work in domain adaptation (DA) for selecting pivots, a comparative study into (a) how the pivots selected using existing strategies differ, and (b) how the pivot selection strategy affects the performance of a target DA task remain unknown. In this paper, we perform a comparative study covering different strategies that use both labelled (available for the source domain only) as well as unlabelled (available for both the source and target domains) data for selecting pivots for UDA. Our experiments show that in most cases pivot selection strategies that use labelled data outperform their unlabelled counterparts, emphasising the importance of the source domain labelled data for UDA. Moreover, pointwise mutual information and frequency-based pivot selection strategies obtain the best performances in two state-of-the-art UDA methods.

Type
Survey Article
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e5
Copyright
© Cambridge University Press, 2018 

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References

Blitzer, J., Dredze, M. & Pereira, F. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the ACL, 440–447.Google Scholar
Blitzer, J., McDonald, R. & Pereira, F. 2006. Domain adaptation with structural correspondence learning. In Proceedings of the EMNLP, 120–128.Google Scholar
Bollegala, D., Mu, T. & Goulermas, J. Y. 2015. Cross-domain sentiment classification using sentiment sensitive embeddings. IEEE Transactions on Knowledge and Data Engineering 28(2), 398–410.Google Scholar
Bollegala, D., Weir, D. & Carroll, J. 2011. Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In Proceedings of the ACL, 132–141.Google Scholar
Bollegala, D., Weir, D. & Carroll, J. 2014. Learning to predict distributions of words across domains. In Proceedings of the ACL, 613–623.Google Scholar
Church, K. W. & Hanks, P. 1990. Word association norms, mutual information, and lexicography’. Computational Linguistics 16(1), 22–29.Google Scholar
Jiang, J. & Zhai, C. 2007. Instance weighting for domain adaptation in nlp. In Proceedings of the ACL, 264–271.Google Scholar
Koehn, P. & Schroeder, J. 2007. Experiments in domain adaptation for statistical machine translation. In Proceedings of the Second Workshop on Statistical Machine Translation, 224–227.Google Scholar
Kübler, S. & Baucom, E. 2011. Fast domain adaptation for part of speech tagging for dialogues. In Proceedings of the RANLP, 41–48.Google Scholar
Li, S. & Zong, C. 2008. Multi-domain sentiment classification. In ACL 2008 (short papers), 257–260.Google Scholar
Liu, Y. & Zhang, Y. 2012. Unsupervised domain adaptation for joint segmentation and POS-tagging. In Proceedings of the COLING, 745–754.Google Scholar
Manning, C. D. & Schütze, H. 1999. Foundations of Statistical Natural Language Processing. MIT Press.Google Scholar
Mansour, R. H., Refaei, N., Gamon, M., Sami, K. & Abdel-Hamid, A. 2013. Revisiting the old kitchen sink: do we need sentiment domain adaptation? In Proceedings of the RANLP, 420–427.Google Scholar
Pan, S. J., Ni, X., Sun, J.-T., Yang, Q. & Chen, Z. 2010. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of WWW, 751–760.Google Scholar
Pang, B., Lee, L. & Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the EMNLP, 79–86.Google Scholar
Schnabel, T. & Schütze, H. 2013. Towards robust cross-domain domain adaptation for part-of-speech tagging. In Proceedings of the IJCNLP, 198–206.Google Scholar
Turney, P. 2006. Similarity of semantic relations. Computational Linguistics 32(3), 379–416.Google Scholar
Turney, P. D. 2001. Minning the web for synonyms: PMI-IR versus LSA on TOEFL. In Proceedings of the ECML-2001, 491–502.Google Scholar
Yu, J. & Jiang, J. 2015. A hassle-free unsupervised domain adaptation method using instance similarity features. In Proceedings of the ACL-IJCNLP, 168–173.Google Scholar
Zhang, Y., Xu, X. & Hu, X. 2015. A common subspace construction method in cross-domain sentiment classification. In Procedings of International Conference on Electronic Science and Automation Control (ESAC), 48–52.Google Scholar