Text Semantic Mining Model Based on the Algebra of Human Concept Learning

Text Semantic Mining Model Based on the Algebra of Human Concept Learning
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基于人类概念学习代数的文本语义挖掘模型

DOI:
10.4018/jcini.2011040105
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发表时间:
2011-04
影响因子:
0.9
通讯作者:
Xiangfeng Luo
Xiangfeng Luo
中科院分区:
--
文献类型:
--
作者:
Jun Zhang;Xiangfeng Luo

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随着Web的发展,对Web上大量文本知识的处理变得越来越重要,但也面临着一些挑战,其中之一就是如何找到尽可能多的语义来表示文本知识。由于文本语义挖掘过程也是文本的知识表示过程,本文提出了一种基于人类概念学习代数的文本语义挖掘模型TSMM,该模型既承载了丰富的语义,又能以较低的复杂度自动构建。在此基础上,引入人类概念学习的代数,使TSMM包含丰富的语义。然后讨论了TSMM的形式化和构建过程。此外,本文还提出了三种基于TSMM的推理规则。最后,通过实验和与现有文本表示模型的比较,表明该模型的性能优于其他模型。
Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model TSMM based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.
DOI: --
发表时间: 2005
期刊: --
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