关键词: 问题分类, 概念漂移, 类标签聚类

Abstract: Being key step of the community question answer system, question classification analyzes natural language questions and returns specified and proper categories. Concerning the problems of network community, such as large taxonomies of categories (>1 000), label hierarchy and vulnerability to time evolution, this paper proposes two different drifting granularity methods, and uses ensemble learning of classifiers built with data in different moments, which improves accuracy and efficiency evidently. Moreover, in view of feature set confusion problem caused by overabundant class labels in one base classifier, the paper proposes a plus enhancer that clusters class labels based on error rate of base classifiers and confusion matrix, which raises classification accuracy further.

Key words: question classification, concept drift, class label cluster