A Conception-Based Approach to Automatic Subject Term Assignment for Scientific Journal Articles

This study proposes a conception-based approach to automatic subject term assignment when using Text Classification (TC) techniques. From the perspective of conceptual and theoretical views of subject indexing, this study identifies three conception-based approaches, Domain-Oriented, Document-Oriented, and Content-Oriented, in conjunction with eight semantic sources in typical scientific journal articles. Based on the identification of semantic sources and conception-based approaches, the experiment explores the significance of individual semantic sources and conception-based approaches for the effectiveness of subject term assignment. The results of the experiment demonstrate that some semantic sources and conception-based approaches are better performers than the full text-based approach which has been dominant in TC fields. In fact, this study indicates that subject terms are better assigned by TC techniques when the indexing conceptions are considered in conjunction with semantic sources

A framework of automatic subject term assignment for text categorization: An indexing conception-based approach

The purpose of this study is to examine whether the understandings of subject-indexing processes con-ducted by human indexers have a positive impact on the effectiveness of automatic subject term assign-ment through text categorization (TC). More specifically, human indexers ’ subject-indexing approaches, or con-ceptions, in conjunction with semantic sources were explored in the context of a typical scientific journal arti-cle dataset. 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, two research questions were explored: To what extent are semantic sources effective? To what extent are indexing concep

A Framework of Automatic Subject Term Assignment: An Indexing Conception-Based Approach

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