在已有的英语语义词典及大量训练集的基础上，应用机器学习、数据挖掘等技术进行知识获取并最终形成若干个概念推理网。

Machine learning and data mining techniques are applied to acquire knowledge and build a concept reasoning network based on semantic dictionary and large training set.

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一般信度网的精确推理是一个NPC问题。

Exact inference on general belief networks is a NPC problem.

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本论文详细研究了信度网精确推理、信度网学习和信度网分类有关内容。

This dissertation focuses on efficient exact inference on belief networks, learning belief networks from data, and classification using belief networks.

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