Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33952
Title: Active learning for cross-lingual sentiment classification
Authors: Li, S
Wang, R
Liu, H
Huang, CR 
Issue Date: 2013
Publisher: Springer Verlag
Source: Communications in computer and information science, 2013, v. 400, p. 236-246 How to cite?
Journal: Communications in Computer and Information Science 
Abstract: Cross-lingual sentiment classification aims to predict the sentiment orientation of a text in a language (named as the target language) with the help of the resources from another language (named as the source language). However, current cross-lingual performance is normally far away from satisfaction due to the huge difference in linguistic expression and social culture. In this paper, we suggest to perform active learning for cross-lingual sentiment classification, where only a small scale of samples are actively selected and manually annotated to achieve reasonable performance in a short time for the target language. The challenge therein is that there are normally much more labeled samples in the source language than those in the target language. This makes the small amount of labeled samples from the target language flooded in the aboundance of labeled samples from the source language, which largely reduces their impact on cross-lingual sentiment classification. To address this issue, we propose a data quality controlling approach in the source language to select high-quality samples from the source language. Specifically, we propose two kinds of data quality measurements, intra- and extra-quality measurements, from the certainty and similarity perspectives. Empirical studies verify the appropriateness of our active learning approach to cross-lingual sentiment classification.
Description: 2nd CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2013, Chongqing, 15-19 November 2013
URI: http://hdl.handle.net/10397/33952
ISBN: 9783642416439
ISSN: 1865-0929
DOI: 10.1007/978-3-642-41644-6_22
Appears in Collections:Conference Paper

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