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

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 259 - 262
• Main Authors: Wang, Bo, Wang, Houfeng
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Applied computing > Document management and text processing > Document capture > Document analysis; Computing methodologies > Artificial intelligence > Natural language processing; Information systems > Information retrieval > Document representation; Information systems > Information retrieval > Document representation > Content analysis and feature selection; Mathematics of computing > Probability and statistics > Probabilistic algorithms; Mathematics of computing > Probability and statistics > Probabilistic reasoning algorithms > Markov-chain Monte Carlo methods; Mathematics of computing > Probability and statistics > Probabilistic reasoning algorithms > Sequential Monte Carlo methods
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.32

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 crosstraining 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..