An automatic approach to weighted subject indexing-an empirical study in the biomedical domain.
Subject indexing is an intellectually intensive process that has many inherent uncertainties. Existing manual subject indexing systems generally produce binary outcomes for whether or not to assign an indexing term. This does not sufficiently reflect the extent to which the indexing terms are associ...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 66; no. 9; pp. 1776 - 1785 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Sep2015
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=108697283&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 108697283 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Sep2015 vid: 66 iid: 9 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 108697283 108697283 108697283 10.1002/asi.23290 108697283 ppf: 1776 ppct: 9 formats: tig: atl: An automatic approach to weighted subject indexing-an empirical study in the biomedical domain. aug: au: Lu, Kun Mao, Jin affil: School of Library and Information Studies, University of Oklahoma, 401 West Brooks, Norman OK, 73019 sug: subj: Medical Literature Abstracting and Indexing Methods Subject Headings Algorithms Human Vocabulary, Controlled Decision Making Information Retrieval ab: Subject indexing is an intellectually intensive process that has many inherent uncertainties. Existing manual subject indexing systems generally produce binary outcomes for whether or not to assign an indexing term. This does not sufficiently reflect the extent to which the indexing terms are associated with the documents. On the other hand, the idea of probabilistic or weighted indexing was proposed a long time ago and has seen success in capturing uncertainties in the automatic indexing process. One hurdle to overcome in implementing weighted indexing in manual subject indexing systems is the practical burden that could be added to the already intensive indexing process. This study proposes a method to infer automatically the associations between subject terms and documents through text mining. By uncovering the connections between MeSH descriptors and document text, we are able to derive the weights of MeSH descriptors manually assigned to documents. Our initial results suggest that the inference method is feasible and promising. The study has practical implications for improving subject indexing practice and providing better support for information retrieval. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|