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Automatic document indexing in large medical collections

Petrakis Evripidis, Chliaoutakis Angelos, Zervanou Kalliopi, Milios Evangelos E.

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URIhttp://purl.tuc.gr/dl/dias/8872D628-35E3-4EE7-81BF-C4744B128CFF-
Identifierhttps://doi.org/10.1145/1183568.1183570-
Languageen-
TitleAutomatic document indexing in large medical collectionsen
CreatorPetrakis Evripidisen
CreatorΠετρακης Ευριπιδηςel
CreatorChliaoutakis Angelosen
CreatorΧλιαουτακης Αγγελοςel
CreatorZervanou Kalliopien
CreatorMilios Evangelos E.en
PublisherAssociation for Computing Machineryen
Content SummaryTerm extraction relates to extracting the most characteristic or important terms (words or phrases) in a document. This information is commonly used for improving the accuracy of document indexing and retrieval in large text collections. It also allows for faster and better understanding of the contents of a document collection without first browsing through the contents of its documents. This paper presents AMTEx an automatic term extraction method, specifically designed for the automatic indexing of documents in large medical collections such as MEDLINE, the premier bibliographic database of the U.S. National Library of Medicine (NLM). AMTEx combines MeSH, the terminological thesaurus resource of NLM, with a well-established method for extraction of domain terms, the C/NC-value method. The performance evaluation of various AMTEx configurations in the indexing task is measured against the current state-of-the-art, the MMTx method. The experimental results on a subset of MEDLINE documents demonstrate that AMTEx achieves better precision and recall than MMTx.en
Type of ItemΠερίληψη Δημοσίευσης σε Συνέδριοel
Type of ItemConference Paper Abstracten
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-01-
Date of Publication2006-
Bibliographic CitationAngelos Hliaoutakis, Kalliopi Zervanou, Euripides G.M. Petrakis, Evangelos Milios, "Automatic Document Indexing in Large Medical Collections" , in ACM International Workshop on Health Information and Knowledge Management (HIKM 2006), 2006, pp. 1-8. doi: 10.1145/1183568.1183570en

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