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Applying Question Classification to Yahoo! Answers

机译:将问题分类应用于雅虎!答案

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摘要

Question classification is an important part in modern Question Answering systems. Most approaches to question classification are based on handcrafted rules. Recent studies classify simple questions using machine learning techniques and recommends SVM as on of the best performing classifiers. This study applies a hierarchical classifier based on the SVM machine learning algorithm on questions posed by users, drawn from Yahoo! Answers. The significance of this study is that we attempted to directly classify complex questions with multiple sentence questions posed by real users. We report the accuracy achieved using both a coarse-grained classifier and fine-grained classifier to illustrate the effectiveness of our approach on complex questions. We also present a confusion matrix to analyze the results made by our classifier.
机译:问题分类是现代问题应答系统中的重要组成部分。大多数问题分类方法基于手工制定的规则。最近的研究使用机器学习技术对简单的问题进行分类,并推荐SVM作为最佳执行分类器。本研究适用于由用户提出的用户提出的SVM机器学习算法的分层分类器,从Yahoo!绘制答案。本研究的重要性是,我们试图直接对具有由真实用户构成的多句问题进行复杂的问题。我们报告了使用粗粒粒子分类器和细粒度分类器实现的准确性,以说明我们对复杂问题的方法的有效性。我们还提出了一种混乱的矩阵来分析我们的分类器所做的结果。

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