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Question Classification Based on Focus

机译:基于焦点的问题分类

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摘要

Question classification plays an important role in Question Answer system. This paper proposes a method based on question focus, which combines advantages of both rulebased methods and statistical methods. The question focus is the kernel of a question and represents the form and content of the doubt. Firstly, question focus definition is given according to linguistics and extraction method is described based on dependency analysis and semantic role labeling on which both depends statistical machine learning. And then, classifying questions with same focus to one category, finer question taxonomy without unreliable human effects is introduced with support of domain ontology. In order to evaluate contributions of question focus, a classifier using question focus is designed and implemented in a practical QA system in restricted domain of computer hardware. Experimental result shows efficiency of question focus and contributions to improve accuracy of question classification and the overall performance of QA.
机译:问题分类在问答系统中起着重要的作用。本文提出了一种基于问题关注的方法,该方法结合了基于规则的方法和统计方法的优点。问题重点是问题的核心,代表疑问的形式和内容。首先,根据语言学给出问题焦点的定义,并基于依赖关系分析和语义角色标记来描述提取方法,两者都依赖于统计机器学习。然后,在领域本体本体的支持下,将具有相同重点的问题归为一类,引入了没有人为影响的更精细的问题分类法。为了评估问题焦点的贡献,在计算机硬件的受限领域中,在实用的质量保证系统中设计并实现了使用问题焦点的分类器。实验结果表明,问题关注点的效率和对提高问题分类准确性和质量保证总体绩效的贡献。

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