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Automatic subject indexing of text
Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences. (Library and Information Science)ORCID iD: 0000-0003-4169-4777
2017 (English)In: ISKO: Encyclopedia of Knowledge Organization / [ed] Birger Hjørland, Claudio Gnoli, International Society for Knowledge Organization , 2017Chapter in book (Refereed)
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 entry automatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOS) 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. This, however, is dependent on availability of training corpora with documents already categorized which are in many cases not there. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. It does not require training documents, but the reported automatically extracted terms and structures are not always of good quality, reflecting the underlying problems of the natural language; also, they both change when new documents are added to the collection and this mutability may not be user-friendly. Document classification re-uses the intellectual effort invested into creating KOSs 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
International Society for Knowledge Organization , 2017.
National Category
Information Studies
Research subject
Humanities, Library and Information Science
Identifiers
URN: urn:nbn:se:lnu:diva-68349OAI: oai:DiVA.org:lnu-68349DiVA, id: diva2:1149085
Available from: 2017-10-13 Created: 2017-10-13 Last updated: 2017-10-30Bibliographically approved

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Golub, Koraljka

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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