A Structural Support Vector Method for Extracting Contexts and Answers of Questions from Online Forums

Wen-Yun Yang, Yunbo Cao, Chin-Yew Lin

Abstract: This paper addresses the issue of extracting contexts and answers of questions from post discussion of online forums. We propose a novel and unified model by customizing the structural Support Vector Machine method. Our customization has several attractive properties: (1) it gives a comprehensive graphical representation of thread discussion. (2) It designs special inference algorithms instead of general-purpose ones. (3) It can be readily extended to different task preferences by varying loss functions. Experimental results on a real data set show that our methods are both promising and flexible.