问题分类是问答系统中重要的组成部分,问题分类结果的好坏直接影响问答系统的质量。本文提出了一种用于问题分类的特征提取的新方法,该方法主要使用句法分析的结果,提取问题的主干和疑问词及其附属成分作为分类的特征,此方法大幅度地减少了噪音,突出了问题分类的主要特征,利用贝叶斯分类器分类,有效地提高了问题分类的精度。实验结果证明了该方法的有效性,大类和小类的分类精度分别达到了86.62%和71.92%,取得了较好的效果。
Abstract
Question classification is very important for question answering ,and the result of question classification directly affects the quality of question answering. This paper presents a new method on feature extraction for question classification. The output of syntactic parsing is used in this method to extract the Subject-Predicate structure as well as interrogative words and their adjunctive parts as features for classification , leading to substantial reduction in noise ,and emphasis on the main features of question classification. A bayesian classifier is used in classification ,which effectively increases the precision of question classification. The experimental result validates the effectiveness of this method : the classification precision of coarse classes and fine classes reach 86.62% and 71.92% respectively ,which attains the expected effects.
关键词
计算机应用 /
中文信息处理 /
问答系统 /
问题分类 /
特征提取 /
句法分析
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Key words
computer application /
Chinese information processing /
question answering system /
question classification /
feature extraction /
syntactic parsing
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参考文献
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脚注
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基金
国家自然科学基金资助项目(60435020)
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