01826nas a2200241   4500000000100000000000100001008004100002260001200043653003200055653002700087653002100114653002700135100002100162700001600183700002800199700003000227245007300257856009500330300001000425490000600435520112900441022001401570       2019                            d  c03/201910aNatural Language Processing10aBiomedical Terminology10aTerm Recognition10aInformation Extraction1 aTeófilo Redondo1 aJulia Díaz1 aAntonio Moreno Sandoval1 aLeonardo Campillos Llanos00aBiomedical Term Extraction: NLP Techniques in Computational Medicine  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_4_6_pdf_46381.pdf  a51-590 v53 aArtificial 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.  a1989-1660