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Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-Training

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2 Author(s)
Bo Wang ; Peking Univ., Beijing ; Houfeng Wang

We investigate the problem of identifying both product properties and opinion words for sentences in a unified process when only a much small labeled corpus is available. Naive Bayesian method is used in this process. Specifically, considering the fact that product properties and opinion words usually co-occur with high frequency in product review articles, a cross- training method is proposed to bootstrap both of them, in which the two sub-tasks are boosted by each other iteratively. Experiment results show that with a much small labeled corpus cross-training could produce both product properties and opinion words which are very close to what Naive Bayesian Classifiers could do with a large labeled corpus..

Published in:

Web Intelligence, IEEE/WIC/ACM International Conference on

Date of Conference:

2-5 Nov. 2007