Mining learning-dependency between knowledge units from text
Mining learning-dependency between knowledge units from text
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DOI：
10.1007/s00778-010-0198-2
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Jun Liu;Lu Jiang;Zhaohui Wu;Q. Zheng;Ya-nan Qian
中科院分区：
文献类型：
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作者：
Jun Liu;Lu Jiang;Zhaohui Wu;Q. Zheng;Ya-nan Qian
识别知识单元(KU)之间的学习依赖是导航学习的基本要求。基于链接挖掘的方法缺乏发现文本中以线性方式排列的知识单元之间的依赖关系的能力。本文提出了一种从文本文档中挖掘知识单元之间的学习依赖关系的方法。该方法基于我们从知识单元中发现和研究的两个特征以及它们之间的学习依赖关系。它们分别是领域术语的分布不对称性和学习依赖的局部性。我们的方法分为三个阶段，(1)通过计算领域术语的分布不对称性来建立文档关联关系。(2)通过度量依赖关系的局部性来生成候选KU对。(3)使用分类算法识别KU对之间的学习依赖关系。实验结果表明，该方法有效地提取了学习依赖，降低了计算复杂度。
Identifying learning-dependency among the knowledge units (KU) is a preliminary requirement of navigation learning. Methods based on link mining lack the ability of discovering such dependencies among knowledge units that are arranged in a linear way in the text. In this paper, we propose a method of mining the learning- dependencies among the KU from text document. This method is based on two features that we found and studied from the KU and the learning-dependencies among them. They are the distributional asymmetry of the domain terms and the local nature of the learning-dependency, respectively. Our method consists of three stages, (1) Build document association relationship by calculating the distributional asymmetry of the domain terms. (2) Generate the candidate KU-pairs by measuring the locality of the dependencies. (3) Use classification algorithm to identify the learning-dependency between KU-pairs. Our experimental results show that our method extracts the learning-dependency efficiently and reduces the computational complexity.