The nature of indexing: how humans and machines analyze messages and texts for retrieval. Part II: Machine indexing, and the allocation of human versus machine effort

Anderson, James D.; Pérez-Carballo, José

doi:10.1016/s0306-4573(00)00046-7

Cited by 36 publications

(16 citation statements)

References 29 publications

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“…Some authors, Lancaster (2003) and Mai (1997) among them, analyze this procedure and the problems of identifying subjects. Others, such as Willis & Losee (2013) or Anderson (2001aAnderson ( , 2001b, review the most important aspects of manual and automatic subject indexing and also the differences between both systems.…”

Section: Literature Reviewmentioning

confidence: 99%

A semi-automatic indexing system based on embedded information in HTML documents

et al. 2015

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Purpose -This paper describes and evaluates the tool DigiDoc MetaEdit which allows the semi-automatic indexing of HTML documents. The tool works by identifying and suggesting keywords from a thesaurus according to the embedded information in HTML documents. This enables the parameterization of keyword assignment based on how frequently the terms appear in the document, the relevance of their position, and the combination of both.Design/methodology/approach -In order to evaluate the efficiency of the indexing tool, the descriptors/keywords suggested by the indexing tool are compared to the keywords which have been indexed manually by human experts. To make this comparison a corpus of HTML documents are randomly selected from a journal devoted to Library and Information Science.Findings -The results of the evaluation show that there: (1) is close to a 50% match or overlap between the two indexing systems, however if you take into consideration the related terms and the narrow terms the matches can reach 73%; and (2) the first terms identified by the tool are the most relevant.Originality/value -The tool presented identifies the most important keywords in an HTML document based on the embedded information in HTML documents. Nowadays, representing the contents of documents with keywords is an essential practice in areas such as information retrieval and e-commerce.

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Section: Literature Reviewmentioning

confidence: 99%

A semi-automatic indexing system based on embedded information in HTML documents

et al. 2015

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“…The new version discounts all documents after the first document, regardless of the logarithm base used for discounting. 26 The modified DCG, used in calculating sDCG is:…”

Section: Session-based Discountingmentioning

confidence: 99%

“…The log base, bq , is a single parameter, not the product of two variables. 26 The modification was suggested by the author.…”

Section: Session-based Discountingmentioning

confidence: 99%

“…Extensive discussions of the theory and practice of keyword indexing, and of the relative advantages and disadvantages of keyword versus full text indexing, are available elsewhere [21][22][23][24][25][26][27]. Here we summarize salient points and note that many modern information retrieval systems use a combination of keyword and full text indexing.…”

Section: Introduction To Information Retrieval Systemsmentioning

confidence: 99%

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Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information

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2000

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Semantic component searching allows a user to refine a topical search by indicating a preference for documents containing specific semantic components or by indicating terms that should appear in specific semantic components. 2We investigate four aspects of semantic components in this research. First, we describe lessons learned from using two methods for developing schemas in two domains. Second, we demonstrate use of semantic components to express domainspecific concepts and relationships by mapping a published taxonomy of questions asked by family practice physicians to the semantic component schemas for two document collections about medical care. Third, we report the results of a user study, showing that manual semantic component indexing is comparable to manual keyword indexing with respect to time and perceived difficulty and suggesting that semantic component indexing may be more accurate and consistent than manual keyword indexing. Fourth, we report the results of an interactive searching study, demonstrating the ability of semantic components to enhance search results compared to a baseline system without semantic components.In addition, we contribute a formal description of the semantic components model, a prototype implementation of semantic component indexing software, and a prototype implementation adding semantic components to an existing commercial search engine.Finally, we analyze metrics for evaluating instances of semantic component indexing and keyword indexing and illustrate use of a session-based metric for evaluating multiple-query search sessions.

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“…We adopt a hybrid approach to information retrieval where we combine high-quality, controlled vocabularybased indexing with automatic text-based indexing because decades of research comparing the two techniques has not identified one technique as superior [1][2][3]. There is general recognition that intellectual, controlled indexing and automatic indexing should be used in combination.…”

Section: Introductionmentioning

confidence: 99%

Accelerated indexing in a domain-specific digital library

Delcambre

Price

Lykke

et al. 2006

Proceedings of the 2006 International Conference on Digital Government Research

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The nature of indexing: how humans and machines analyze messages and texts for retrieval. Part II: Machine indexing, and the allocation of human versus machine effort

Cited by 36 publications

References 29 publications

A semi-automatic indexing system based on embedded information in HTML documents

A semi-automatic indexing system based on embedded information in HTML documents

Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information

Accelerated indexing in a domain-specific digital library

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