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A framework of automatic subject term assignment: An indexing conception-based approach.

机译：自动主题词分配框架：一种基于索引概念的方法。

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The purpose of dissertation is to examine whether the understandings of subject indexing processes conducted by human indexers have a positive impact on the effectiveness of automatic subject term assignment through text categorization (TC). More specifically, human indexers' subject indexing approaches or conceptions in conjunction with semantic sources were explored in the context of a typical scientific journal article data set.; Based on the premise that subject indexing approaches or conceptions with semantic sources are important for automatic subject term assignment through TC, this study proposed an indexing conception-based framework. For the purpose of this study, three hypotheses were tested: (1) the effectiveness of semantic sources, (2) the effectiveness of an indexing conception-based framework, and (3) the effectiveness of each of three indexing conception-based approaches (the content-oriented, the document-oriented, and the domain-oriented approaches). The experiments were conducted using a support vector machine implementation in WEKA (Witten, & Frank, 2000).; The experiment results pointed out that cited works, source title, and title were as effective as the full text, while keyword was found more effective than the full text. In addition, the findings showed that an indexing conception-based framework was more effective than the full text. Especially, the content-oriented and the document-oriented indexing approaches were found more effective than the full text. Among three indexing conception-based approaches, the content-oriented approach and the document-oriented approach were more effective than the domain-oriented approach. In other words, in the context of a typical scientific journal article data set, the objective contents and authors' intentions were more focused that the possible users' needs. The research findings of this study support that incorporation of human indexers' indexing approaches or conception in conjunction with semantic sources has a positive impact on the effectiveness of automatic subject term assignment.
机译：论文的目的是检验人类索引员对主题索引过程的理解是否对通过文本分类（TC）进行自动主题词分配的有效性产生积极影响。更具体地说，在典型的科学期刊文章数据集的背景下，探索了人类索引者的主题索引方法或概念以及语义来源。在基于主题索引的方法或具有语义来源的概念对于通过TC自动分配主题术语的前提下，本研究提出了一种基于索引概念的框架。出于本研究的目的，检验了三个假设：（1）语义源的有效性，（2）基于索引概念的框架的有效性，以及（3）三种基于索引概念的方法中的每种的有效性（面向内容，面向文档和面向领域的方法）。实验是在WEKA（Witten，＆Frank，2000）中使用支持向量机实现的。实验结果表明，被引作品，原文标题和标题与全文一样有效，而关键词被发现比全文更有效。此外，调查结果表明，基于索引概念的框架比全文本更有效。特别是，发现面向内容和面向文档的索引方法比全文索引更有效。在三种基于索引概念的方法中，面向内容的方法和面向文档的方法比面向领域的方法更有效。换句话说，在典型的科学期刊文章数据集的背景下，目标内容和作者的意图更加集中于可能的用户需求。这项研究的研究结果支持将人类索引器的索引方法或概念与语义源结合使用会对自动主题术语分配的有效性产生积极影响。

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