Question classification using support vector machines

10 years 2 months ago
Question classification using support vector machines
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 five machine learning algorithms: Nearest Neighbors (NN), Naïve Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-ofngrams. The experiment results show that with only surface text features the SVM outperforms the other four methods for this task. Further, we propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. We describe how the tree kernel can be computed efficiently by dynamic programming. The performance of our approach is promising, when tested on the questions from the TREC QA track. Categories and Subject Descriptors
Dell Zhang, Wee Sun Lee
Added 05 Jul 2010
Updated 05 Jul 2010
Type Conference
Year 2003
Authors Dell Zhang, Wee Sun Lee
Comments (0)