Question classification using support vector machines

Dell Zhang, Wee Sun Lee
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引用次数: 684

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), Naive 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.
使用支持向量机进行问题分类
问题分类对问题回答非常重要。本文介绍了我们通过机器学习方法在自动问题分类方面的研究工作。我们实验了五种机器学习算法:最近邻(NN)、朴素贝叶斯(NB)、决策树(DT)、窗口稀疏网络(SNoW)和支持向量机(SVM),使用两种特征:词袋和ngrams袋。实验结果表明,仅在表面文本特征下,支持向量机在此任务中的表现优于其他四种方法。此外,我们建议使用一种称为树核的特殊核函数来使支持向量机能够利用问题的语法结构。我们描述了如何通过动态规划有效地计算树核。在对TREC QA轨道上的问题进行测试时,我们的方法的性能是有希望的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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