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Co-Training for Cross-Lingual Sentiment Classification
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BibTeX
@MISC{Wan_co-trainingfor,
author = {Xiaojun Wan},
title = {Co-Training for Cross-Lingual Sentiment Classification},
year = {}
}
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Abstract
The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine translation services are used for eliminating the language gap between the training set and test set, and English features and Chinese features are considered as two independent views of the classification problem. We propose a cotraining approach to making use of unlabeled Chinese data. Experimental results show the effectiveness of the proposed approach, which can outperform the standard inductive classifiers and the transductive classifiers. 1
Keyphrases
cross-lingual sentiment classification chinese sentiment classification research progress language gap training data test set training set independent view machine translation service available english corpus standard inductive classifier english corpus transductive classifier classification problem english feature chinese sentiment corpus chinese feature experimental result unlabeled chinese data available english sentiment corpus cotraining approach
