lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Automatic Subject Indexing of Text
Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.ORCID iD: 0000-0003-4169-4777
2019 (English)In: Knowledge organization, ISSN 0943-7444, Vol. 46, no 2, p. 104-121Article, review/survey (Refereed) Published
Abstract [en]

Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collections, and enhance consistency of the metadata. In this work, automatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are discussed, in terms of their similarities and differences, advantages and disadvantages for automatic assigned indexing from KOSs: "text categorization," "document clustering," and "document classification." Text categorization is perhaps the most widespread, machine-learning approach with what seems generally good reported performance. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. Document classification re-uses the intellectual effort invested into creating a KOS for subject indexing and even simple string-matching algorithms have been reported to achieve good results, because one concept can be described using a number of different terms, including equivalent, related, narrower and broader terms. Finally, applicability of automatic subject indexing to operative information systems and challenges of evaluation are outlined, suggesting the need for more research.

Place, publisher, year, edition, pages
Nomos Verlagsgesellschaft, 2019. Vol. 46, no 2, p. 104-121
Keywords [en]
indexing, subject, terms, document, documents, automatic, classification
National Category
Information Studies
Research subject
Humanities, Library and Information Science
Identifiers
URN: urn:nbn:se:lnu:diva-88845DOI: 10.5771/0943-7444-2019-2-104ISI: 000480655300003OAI: oai:DiVA.org:lnu-88845DiVA, id: diva2:1346908
Available from: 2019-08-29 Created: 2019-08-29 Last updated: 2019-08-29Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Golub, Koraljka

Search in DiVA

By author/editor
Golub, Koraljka
By organisation
Department of Cultural Sciences
In the same journal
Knowledge organization
Information Studies

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 12 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf