Question Classification: An Approach using Support Vector Machine and Particle Swarm Optimization

摘要

Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with two machine learning algorithms: support vector machine and back propagation feed forward artificial neural network (BPFFANN) using three kinds of features: lexical, semantic and syntatic. The experiment results show that the SVM perform better than the BPFFANN. Further, we propose PSO technique for optimizing the parameters for SVM and also it will given the significant improvement of the classification technique with improved classification accuracy. The performance of our approach is promising, when tested on the questions from the UIUC dataset.