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

  • Wenxiu Xie
  • Dongfa GaoEmail author
  • Tianyong HaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)

Abstract

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.

Keywords

Answer type classification Question target word Question target feature 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61403088), the programs of Personalized Health Service Public Platform based on Open and Big Data (No. 2014B010118005), Ancient Literature Knowledge base Platform for the Inheritance and Development of Traditional Chinese Medicine (No. 2014A020221039) and Innovative School Project in Higher Education of Guangdong (No. YQ2015062).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Science and TechnologyGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.Collaborative Innovation Center for 21st-Century Maritime Silk Road StudiesGuangdong University of Foreign StudiesGuangzhouChina

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