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  • × author_ss:"Gil-Leiva, I."
  • × theme_ss:"Automatisches Indexieren"
  1. Gil-Leiva, I.; Munoz, J.V.R.: Analisis de los descriptores de diferentes areas del conocimiento indizades en bases de datos del CSIC : Aplicacion a la indizacion automatica (1997) 0.00 
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    Abstract
    Studies the value of scientific articles' titles and abstracts as sources of terms for document indexing in relation to 6 areas of knowledge: library and information science, medicine, chemistry, biology, psychology and physics, indexed in the databases ISOC, IME and ICYT of the CSIC. Also examines the syntagmatic structures of the indexing terms found in the field 'descriptors'. as well as the relationship between length of document and number of descriptors. Concludes that if the abstracts are not well made and the titles are not precise, they are not definitive sources for the extractions of concepts; the most common syntactic structure is the noun phrase, followed by noun+adjective and noun+noun; and no significant relationship was found between length of document and number of descriptors assigned to it
    Footnote
    Übers. d. Titels: Descriptors analysis on different knowledge ares in CSIC databases: application on automatic indexing
  2. Souza, R.R.; Gil-Leiva, I.: Automatic indexing of scientific texts : a methodological comparison (2016) 0.00 
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    Series
    Advances in knowledge organization; vol.15
    Source
    Knowledge organization for a sustainable world: challenges and perspectives for cultural, scientific, and technological sharing in a connected society : proceedings of the Fourteenth International ISKO Conference 27-29 September 2016, Rio de Janeiro, Brazil / organized by International Society for Knowledge Organization (ISKO), ISKO-Brazil, São Paulo State University ; edited by José Augusto Chaves Guimarães, Suellen Oliveira Milani, Vera Dodebei
  3. Gil-Leiva, I.: SISA-automatic indexing system for scientific articles : experiments with location heuristics rules versus TF-IDF rules (2017) 0.00 
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    Abstract
    Indexing is contextualized and a brief description is provided of some of the most used automatic indexing systems. We describe SISA, a system which uses location heuristics rules, statistical rules like term frequency (TF) or TF-IDF to obtain automatic or semi-automatic indexing, depending on the user's preference. The aim of this research is to ascertain which rules (location heuristics rules or TF-IDF rules) provide the best indexing terms. SISA is used to obtain the automatic indexing of 200 scientific articles on fruit growing written in Portuguese. It uses, on the one hand, location heuristics rules founded on the value of certain parts of the articles for indexing such as titles, abstracts, keywords, headings, first paragraph, conclusions and references and, on the other, TF-IDF rules. The indexing is then evaluated to ascertain retrieval performance through recall, precision and f-measure. Automatic indexing of the articles with location heuristics rules provided the best results with the evaluation measures.
    Content
    Beitrag in einem Special Issue "New Trends for Knowledge Organization, Guest Editor: Renato Rocha Souza ".

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