Language and representation in information retrieval
Language and representation in information retrieval
Representation and learning in information retrieval
Representation and learning in information retrieval
The concept of “subject” in information science
Journal of Documentation
Journal of the American Society for Information Science
Toward a new horizon in information science: domain-analysis
Journal of the American Society for Information Science
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
A patent search and classification system
Proceedings of the fourth ACM conference on Digital libraries
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Machine learning for information architecture in a large governmental website
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Combining structural and citation-based evidence for text classification
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Journal of the American Society for Information Science and Technology
Analysis in indexing: document and domain centered approaches
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
A hybrid generative/discriminative approach to text classification with additional information
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Patent document categorization based on semantic structural information
Information Processing and Management: an International Journal
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
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The purpose of this study 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 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 conceptions effective? The experiments were conducted using a Support Vector Machine implementation in WEKA (I.H. Witten & E. Frank, [2000]). Using F-measure, the experiment results showed that cited works, source title, and title were as effective as the full text while a 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. The content-oriented and the document-oriented indexing approaches especially 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 dataset, the objective contents and authors' intentions were more desirable for automatic subject term assignment via TC than the possible users' needs. The 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. © 2010 Wiley Periodicals, Inc.