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



Cui, Xia ORCID: 0000-0002-1726-3814, Al-Bazzaz, Noor, Bollegala, Danushka and Coenen, Frans
(2018) A comparative study of pivot selection strategies for unsupervised cross-domain sentiment classification. KNOWLEDGE ENGINEERING REVIEW, 33. e5-e5.

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Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 01 Aug 2018 07:32
Last Modified: 19 Jan 2023 01:29
DOI: 10.1017/S0269888918000085
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3024444