Biomedical Term Extraction: NLP Techniques in Computational Medicine
Entity
UAM. Departamento de Lingüística, Lenguas Modernas, Lógica y Fª de la Ciencia y Tª de la Literatura y Literatura ComparadaPublisher
IMAI SoftwareDate
2018-04-06Citation
10.9781/ijimai.2018.04.001
International Journal of Interactive Multimedia and Artificial Intelligence 5.4 (2019): 51-59
ISSN
1989-1660DOI
10.9781/ijimai.2018.04.001Editor's Version
https://doi.org/10.9781/ijimai.2018.04.001Subjects
Biomedical Terminology; Natural Language Processing; Term Recognition; Information Extraction; InformáticaAbstract
Artificial Intelligence (AI) and its branch Natural Language Processing (NLP) in particular are main contributors
to recent advances in classifying documentation and extracting information from assorted fields, Medicine
being one that has gathered a lot of attention due to the amount of information generated in public professional
journals and other means of communication within the medical profession. The typical information extraction
task from technical texts is performed via an automatic term recognition extractor. Automatic Term Recognition
(ATR) from technical texts is applied for the identification of key concepts for information retrieval and,
secondarily, for machine translation. Term recognition depends on the subject domain and the lexical patterns
of a given language, in our case, Spanish, Arabic and Japanese. In this article, we present the methods and
techniques for creating a biomedical corpus of validated terms, with several tools for optimal exploitation of
the information therewith contained in said corpus. This paper also shows how these techniques and tools have been used in a prototype.
Files in this item
Google Scholar:Moreno Sandoval, Antonio
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Díaz García, Julia
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Campillos Llanos, Leonardo
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Redondo, Teófilo
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