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Automatic subject indexing of text
Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för kulturvetenskaper (KV). (Library and Information Science)ORCID-id: 0000-0003-4169-4777
2017 (engelsk)Inngår i: ISKO: Encyclopedia of Knowledge Organization / [ed] Birger Hjørland, Claudio Gnoli, International Society for Knowledge Organization , 2017Kapittel i bok, del av antologi (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
International Society for Knowledge Organization , 2017.
HSV kategori
Forskningsprogram
Humaniora, Biblioteks- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-68349OAI: oai:DiVA.org:lnu-68349DiVA, id: diva2:1149085
Tilgjengelig fra: 2017-10-13 Laget: 2017-10-13 Sist oppdatert: 2017-10-30bibliografisk kontrollert

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