TY - JOUR
KW - Natural Language Processing
KW - Biomedical Terminology
KW - Term Recognition
KW - Information Extraction
AU - Teófilo Redondo
AU - Julia Díaz
AU - Antonio Moreno Sandoval
AU - Leonardo Campillos Llanos
AB - 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.
IS - Special Issue on Artificial Intelligence Applications
M1 - 4
N2 - 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.
PY - 2019
SP - 51
EP - 59
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Biomedical Term Extraction: NLP Techniques in Computational Medicine
UR - http://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_4_6_pdf_46381.pdf
VL - 5
SN - 1989-1660
ER -