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This dissertation is part of a project of a tool named FIP (an Intelligent Tool for Research Supporting). FIP is a tool for retrieval, organization, and mining large document collections. In the context of FIP diverse techniques from Information Retrieval, Data Mining, Information Visualization, and particularly Information Extraction, focus of this work, are used. Information Extraction systems deal with unstructured data looking for specific information in a document or document collection, extracting and structuring them in order to facilitate their use. The specific objective presented in this dissertation is automatically to induce a set of rules for information extraction from scientific articles. The proposed extraction system initially analyzes and extracts information from the body of the articles (heading, authors, affiliation, abstract, and keywords) and then extracts information from each reference in its bibliographical references. The proposed approach for information extraction from references is a new technique based on the strategy of part-of-speech tagging. As the outcome of the extraction process, a database with extracted and structured information in XML format is made available for the FIP or any other application. The system has been evaluated using measures of Precision, Recall and F-measure, reaching good results compared to similar systems.

Extração de informação de artigos científicos: uma abordagem baseada em indução de regras de etiquetagem