A structural support vector method for extracting contexts and answers of questions from online forums

Authors:
Yunbo Cao;Wen-Yun Yang;Chin-Yew Lin;Yong Yu
Affiliations:
Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China and Web Intelligence Group, Microsoft Research Asia, Beijing 100190, China;Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China;Web Intelligence Group, Microsoft Research Asia, Beijing 100190, China;Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
Venue:
Information Processing and Management: an International Journal
Year:
2011

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Abstract

This article addresses the issue of extracting contexts and answers of questions from posts of online discussion forums. In previous work, general-purpose graphical models have been employed without any customization to this specific extraction problem. Instead, in this article, we propose a unified approach to context and answer extraction by customizing the structural support vector machine method. The customization enables our proposal to explore various relations among sentences of posts and complex structures of threads. We design new inference algorithms to find or approximate the most violated constraint by utilizing the specific structure of forum threads, which enables us to efficiently find the global optimum of the customized optimizing problem. We also optimize practical performance measures by varying loss functions. Experimental results show that our methods are both promising and flexible.