Active Learning for Cross Language Text Categorization

Abstract

Cross Language Text Categorization (CLTC) is the task of assigning class labels to documents written in a target language (e.g. Chinese) while the system is trained using labeled examples in a source language (e.g. English). With the technique of CLTC, we can build classifiers for multiple languages employing the existing training data in only one language, therefore avoid the cost of preparing training data for each individual language. One challenge for CLTC is the culture differences between languages, which causes the classifier trained on the source language doesn't perform well on the target language. In this paper, we propose an active learning algorithm for CLTC, which takes full advantage of both labeled data in the source language and unlabeled data in the target language. The classifier first learns the classification knowledge from the source language, and then learns the cultural dependent knowledge from the target language. In addition, we extend our algorithm to double viewed form by considering the source and target language as two views of the classification problem. Experiments show that our algorithm can effectively improve the cross language classification performance.