A Feature Extraction and Expansion-Based Approach for Question Target Identification and Classification

Detecting question target words from user questions is a crucial step in question target classification as it can precisely reflect the users’ potential need. In this paper we propose a concise approach named as QTF_EE to identify question target words, extract question target features and expand the features for question target classification. Based on two publicly available datasets that are labeled with 50 answer types, we compare the QTF_EE approach with 12 conventional classification methods such as bag-of-words and Random Forest as baseline methods. The results show that the QTF_EE approach outperforms the baselines and is able to improve the question target classification performance to an accuracy of 87.4%, demonstrating its effectiveness in question target identification.