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Integrating of Learning Mechanisms for Information Retrieval

机译:整合信息检索学习机制

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摘要

In this paper, we describe our information retrieval system which allows a convivial access to databases. It is based on multi-experts architecture using a database management systems and a blackboard to control the progressive analysis of the user's sentence. We have integrated a numerical method based on fuzzy rules to deal with uncertainty. This method is used to optimize the analysis process and the cooperation between the different experts. On the other hand, the goal of our recent researches is to ease or eliminate the knowledge-acquisition bottleneck for expert system creation and to make a connectionist model behave as much as possible like an expert system. We describe our experience using neural networks to represent the knowledge bases of the different experts (i.e., lexical entries expert, homographs expert, template expert, grammatical word expert and words expert).
机译:在本文中,我们描述了我们的信息检索系统,该系统允许以欢乐的方式访问数据库。它基于多专家体系结构,使用数据库管理系统和黑板来控制用户句子的渐进分析。我们已经集成了基于模糊规则的数值方法来处理不确定性。该方法用于优化分析过程以及不同专家之间的合作。另一方面,我们最近的研究目标是缓解或消除专家系统创建的知识获取瓶颈,并使连接主义模型尽可能像专家系统那样工作。我们使用神经网络描述我们的经验,以代表不同专家的知识基础(即词法输入专家,同形异义词专家,模板专家,语法单词专家和单词专家)。

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