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A Predictive Search Method of FAQ Corresponding to a User's Incomplete Inquiry by Statistical Model of Important Words Co-occurrence

机译：基于重要词共现统计模型的用户不完整查询对应的FAQ预测搜索方法

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We address a predictive search of FAQ corresponding to a user's incomplete inquiry that a user is inputting with important words defined in each FAQ. The important words co-occur in a user's inquiries and the rates of the co-occurrences depend on which FAQ the user's inquiry corresponds to. The co-occurrence rates of important words in inquiries are estimated from a statistical model of important words co-occurrence generated with past inquiries and FAQ corresponding to them. When the highest co-occurrence rate of them is larger than a threshold set on each FAQ, the inquiry is regarded as a corresponding FAQ. Experimental results show that the proposed method can improve the recall rate by 40% for short inquiries and the precision rate by 27% for long inquiries.

机译：我们针对与用户输入的每个FAQ中定义的重要单词的用户不完整查询相对应的FAQ进行预测性搜索。用户查询中同时出现的重要单词和同时发生的比率取决于用户查询所对应的FAQ。从与过去的查询和与之对应的常见问题解答生成的重要词共现统计模型中，估计查询中重要词共现率。当它们的最高同时出现率大于每个FAQ上设置的阈值时，该查询被视为对应的FAQ。实验结果表明，该方法可以使短查询的召回率提高40％，长查询的查准率提高27％。

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《Distributed computing and artificial intelligence.》|2012年|p.9-16|共8页
会议地点 Salamanca(ES);Salamanca(ES)
作者
Masaki Samejima; Yuichi Saito; Masanori Akiyoshi; Hironori Oka;
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作者单位

Osaka University, 2-1, Yamadaoka, Suita, Osaka, Japan;

Osaka University, 2-1, Yamadaoka, Suita, Osaka, Japan;

Osaka University, 2-1, Yamadaoka, Suita, Osaka, Japan;

Codetoys K.K., 2-6-8 Nishitenma Kita, Osaka, Japan;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论;人工智能理论;
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A Predictive Search Method of FAQ Corresponding to a User's Incomplete Inquiry by Statistical Model of Important Words Co-occurrence

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