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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: xia.cui@liverpool.ac.uk, noorbahjattayfor@yahoo.com, danushka@liverpool.ac.uk, coenen@liverpool.ac.uk
Noor Al-Bazzaz
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: xia.cui@liverpool.ac.uk, noorbahjattayfor@yahoo.com, danushka@liverpool.ac.uk, coenen@liverpool.ac.uk
Danushka Bollegala
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: xia.cui@liverpool.ac.uk, noorbahjattayfor@yahoo.com, danushka@liverpool.ac.uk, coenen@liverpool.ac.uk
Frans Coenen
Affiliation:
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK e-mail: xia.cui@liverpool.ac.uk, noorbahjattayfor@yahoo.com, danushka@liverpool.ac.uk, coenen@liverpool.ac.uk
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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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