Abstract
The term extraction is an important step in building a resource of indexing and many strong tools are available for many languages. This complex process, which identifies candidate terms may become indexes for annotations, is often subject to the problem of lack of relevance of calculated terms. As a result, extractor terms must be strong to handle the errors and suggest better results, without encumbering the user with too many proposed index. In this respect, we are suggesting a new indexing approach based on a hybrid of terminologies extraction using a filter by removing terms and operates upon corpus of medical texts.
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Benafia, A., Maamri, R., Sahnoun, Z. (2013). A Terminology Indexing Based on Heuristics Using Linguistic Resources for Medical Textual Corpus. In: Kountchev, R., Iantovics, B. (eds) Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Studies in Computational Intelligence, vol 473. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00029-9_19
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DOI: https://doi.org/10.1007/978-3-319-00029-9_19
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