Text Database Discovery on the Web: Neural Net Based Approach

YONG S. CHOI; SUK I. YOO

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Text Database Discovery on the Web: Neural Net Based Approach

机译：Web上的文本数据库发现：基于神经网络的方法

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As large numbers of text databases have become available on the Web, many efforts have been made to solve the text database discovery problem: finding which text databases (out of many candidates) are most likely to provide relevant documents to a given query. In this paper, we propose a neural net based approach to this problem. First, we present a neural net agent that learns about underlying text databases from the user's relevance feedback.

机译：随着大量文本数据库在Web上的可用，已经做出了许多努力来解决文本数据库发现问题：找到哪些文本数据库（在许多候选数据库中）最有可能为给定查询提供相关文档。在本文中，我们提出了一种基于神经网络的方法来解决这个问题。首先，我们介绍一种神经网络代理，该代理从用户的相关性反馈中了解底层文本数据库。

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《Journal of Intelligent Information Systems》 |2001年第1期|p.5-20|共16页
作者
YONG S. CHOI; SUK I. YOO;
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正文语种 eng
中图分类计算技术、计算机技术;
关键词
text database; neural net agent; relevance feedback;

机译：文本数据库;神经网络代理相关性反馈;

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Text Database Discovery on the Web: Neural Net Based Approach

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