Search (13 results, page 1 of 1)

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Source

Cataloging and classification quarterly. 58(2020) no.1, S.28-43

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Source

Cataloging and classification quarterly. 59(2021) no.1, S.53-57

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Abstract

The present bibliography comprises research produced as doctoral dissertations and doctoral theses dealing with library cataloging, classification, and metadata. An attempt has been made to cover all the aspects of these topics so as to match the coverage of this bibliography with the scope of the journal, Cataloging & Classification Quarterly.

Source

Cataloging and classification quarterly. 60(2022) no.1, p.13-18

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Abstract

The aim of this paper is to gauge the cataloging practices within the public library sector seen from the catalog with Norway as a case, based on a sample of records from public libraries and cataloging agencies. Findings suggest that libraries make few changes to records they import from central agencies, and that larger libraries make more changes than smaller libraries. Findings also suggest that libraries catalog and modify records with their patrons in mind, and though the extent is not large, cataloging proficiency is still required in the public library domain, at least in larger libraries, in order to ensure correct and consistent metadata.

Source

Cataloging and classification quarterly. 58(2020) no.2, S.127-148

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Abstract

Metadata creation is the process of recording metadata, that is data essential to the identification and retrieval of any type of resource, including bibliographic resources. Metadata capable of identifying characteristics of an entity have always existed. However, the triggering event that has rewritten and enhanced their value is the digital revolution. Cataloging is configured as an action of creating metadata. While cataloging produces a catalog, that is a list of records relating to various types of resources, ordered and searchable, according to a defined criterion, the metadata process produces the metadata of the resources.

Source

Cataloging and classification quarterly. 60(2022) no.5, p.411-423

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Abstract

This study examines subject and genre representation in metadata that describes information resources created for children and young adult audiences. Both quantitative and limited qualitative analyses were applied to the analysis of WorldCat records collected in 2021 and contributed by the Children's and Young Adults' Cataloging Program at the US Library of Congress. This dataset contains records created several years prior to the data collection point and edited by various OCLC member institutions. Findings provide information on the level and patterns of application of these kinds of metadata important for information access, with a focus on the fields, subfields, and controlled vocabularies used. The discussion of results includes a detailed evaluation of genre and subject metadata quality (accuracy, completeness, and consistency).

Source

Cataloging and classification quarterly. 61(2023) no.1, p.47-66

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Source

Cataloging and classification quarterly. 60(2022) no.1, p.45-68

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Source

Cataloging and classification quarterly. 60(2022) no.6-7, p.514-535

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Source

Cataloging and classification quarterly. 61(2023) no.5-6, S.590-604

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Source

Cataloging and classification quarterly. 59(2021) no.8, p.757-774

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Abstract

Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi-file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real-world data. We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.

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Date

22. 5.2021 12:43:05

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Footnote

Rez. in: Cataloging and classification quarterly. 60(2022) no.3/4, p.329-331 (Erica Zhang).