从教科书中提取概念层次

Shuting Wang, Chen Liang, Zhaohui Wu, Kyle Williams, B. Pursel, Benjamin Bräutigam, Sherwyn Saul, Hannah Williams, Kyle Bowen, C. Lee Giles
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引用次数: 51

摘要

概念层次结构是表示和组织知识的有用工具。随着在线知识资源数量的快速增长,概念层次的自动提取越来越有吸引力。在这里,我们的重点是基于维基百科中的知识从教科书中提取概念。给定一本书,我们使用维基百科作为资源,从书的每个章节中提取重要的概念,并以此为该书构建概念层次结构。我们定义了局部和全局特征,以捕捉教科书中嵌入的局部相关性和全局一致性。为了评估提出的特征和提取的概念层次,我们通过标记每一章节的重要概念,为三本常用的教科书手动构建了概念层次。实验表明,我们提出的局部特征和全局特征比仅使用关键字短语构建概念层次具有更好的性能。此外,我们观察到纳入全局特征可以提高概念排序的精度,并重申了书中的全局一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Concept Hierarchy Extraction from Textbooks
Concept hierarchies have been useful tools for presenting and organizing knowledge. With the rapid growth in the number of online knowledge resources, automatic concept hierarchy extraction is increasingly attractive. Here, we focus on concept extraction from textbooks based on the knowledge in Wikipedia. Given a book, we extract important concepts in each book chapter using Wikipedia as a resource and from this construct a concept hierarchy for that book. We define local and global features that capture both the local relatedness and global coherence embedded in that textbook. In order to evaluate the proposed features and extracted concept hierarchies, we manually construct concept hierarchies for three well used textbooks by labeling important concepts for each book chapter. Experiments show that our proposed local and global features achieve better performance than using only keyphrases to construct the concept hierarchies. Moreover, we observe that incorporating global features can improve the concept ranking precision and reaffirms the global coherence in the book.
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