A Support Vector Machine Based System for Technical Question Classification

Shlok Kumar Mishra, Pranav Kumar, Sujan Kumar Saha

Abstract: This paper presents our attempt on developing a question classification system for technical domain. Question classification system classifies a question into the type of answer it requires and therefore plays an important role in question answering. Although the task is quite popular in general domain, we were unable to find any question classification system that classifies the questions of a technical subject. We defined a technical domain question taxonomy containing six classes. We manually created a dataset containing 1086 questions. Then we identified a set of features suitable for the technical domain. We observed that the parse structure similarity plays an important role in this classification. To capture the parse tree similarity we employed the tree kernel and we proposed a level-wise matching approach. We have used these features and dataset in a support vector machine classifier to achieve 93.22 % accuracy.