Towards effective short text deep classification

Xinruo Sun, Haofen Wang, Yong Yu

Abstract: Recently, more and more short texts (e.g., ads, tweets) appear on the Web. Classifying short texts into a large taxonomy like ODP or Wikipedia category system has become an important mining task to improve the performance of many applications such as contextual advertising and topic detection for micro-blogging. In this paper, we propose a novel multi-stage classification approach to solve the problem. First, explicit semantic analysis is used to add more features for both short texts and categories. Second, we leverage information retrieval technologies to fetch the most relevant categories for an input short text from thousands of candidates. Finally, a SVM classifier is applied on only a few selected categories to return the final answer. Our experimental results show that the proposed method achieved significant improvements on classification accuracy compared with several existing state of art approaches.