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Title: Question Classification using Support Vector Machines
Authors: Zhang, D. 
Lee, W.S. 
Keywords: Kernel method
Machine learning
Question answering
Support vector machine
Text classification
Issue Date: 2003
Citation: Zhang, D.,Lee, W.S. (2003). Question Classification using Support Vector Machines. SIGIR Forum (ACM Special Interest Group on Information Retrieval) (SPEC. ISS.) : 26-32. ScholarBank@NUS Repository.
Abstract: 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-of-ngrams. 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.
Source Title: SIGIR Forum (ACM Special Interest Group on Information Retrieval)
ISSN: 01635840
Appears in Collections:Staff Publications

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