Browsing by Subject "Automatic indexing"

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    Automatic indexing of scientific articles on Library and Information Science with SISA, KEA and MAUI
    (Consejo Superior de Investigaciones Científicas, 2022-10-18) Gil-Leiva, Isidoro; Díaz Ortuño, Pedro; Fernandes Correa, Renato; Información y Documentación
    This article evaluates the SISA (Automatic Indexing System), KEA (Keyphrase Extraction Algorithm) and MAUI (Multi-Purpose Automatic Topic Indexing) automatic indexing systems to find out how they perform in relation to human indexing. SISA’s algorithm is based on rules about the position of terms in the different structural components of the document, while the algorithms for KEA and MAUI are based on machine learning and the statistical features of terms. For evaluation purposes, a document collection of 230 scientific articles from the Revista Española de Document ación Científica published by the Consejo Superior de Investigaciones Científicas (CSIC) was used, of which 30 were used for training tasks and were not part of the evaluation test set. The articles were written in Spanish and indexed by human indexers using a controlled vocabulary in the InDICES database, also belonging to the CSIC. The human indexing of these documents constitutes the baseline or golden indexing, against which to evaluate the output of the automatic indexing systems by comparing terms sets using the evaluation metrics of precision, recall, F-measure and consistency. The results show that the SISA system performs best, followed by KEA and MAUI.
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    SISA: Automatic indexing system for scientific articles. Experiments with location heuristics rules versus TF-IDF rules
    (International Society for Knowledge Organization, 2017) Gil-Leiva, Isidoro; Información y Documentación
    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.