Cross-Language Opinion Target Extraction in Review Texts

Abstract

Opinion target extraction is a subtask of opinion mining which is very useful in many applications. In this study, we investigate the problem in a cross-language scenario which leverages the rich labeled data in a source language for opinion target extraction in a different target language. The English labeled corpus is used as training set. We generate two Chinese training datasets with different features. Two labeling models for Chinese opinion target extraction are learned based on Conditional Random Fields (CRF). After that, we use a monolingual co-training algorithm to improve the performance of both models by leveraging the enormous unlabeled Chinese review texts on the web. Experimental results show the effectiveness of our proposed approach.