A kind of task derived from the knowledge discovery, focused to recognize patterns in large collections of unstructured data (that is, linguistic corpora), which contain relevant information associated to concepts. For solving such task, it is employed different methods based on NLP (e.g., tokenization, lemmatization, POS tagging, syntactic and semantic analysis, and others).
Published in Chapter:
Designing a Concept-Mining Model for the Extraction of Medical Information in Spanish
Olga Acosta (Singularyta SpA, Chile) and César Aguilar (Pontificia Universidad Católica de Chile, Chile)
Copyright: © 2021
|Pages: 17
DOI: 10.4018/978-1-7998-3479-3.ch059
Abstract
This article sketches the development of a method for mining concepts applied on medical corpora in Spanish. Such method is based in the approach formulated by Ananiadou and McNaught, who give a special relevance to the need to create and use natural language processing (NLP) tools, in order to extract information from large collections of documents, such as PubMed (www.ncbi.nlm.nih.gov/pubmed/). Thanks to this repository, projects such as the Corpus Genia (www.geniaproject.org), the MEDIE search engine (www.nactem.ac.uk/medie/), which considers syntactic criteria and semantics to extract medical concepts, or the Open Biological and Biomedical Ontology Project (http://obofoundry.org/), which focuses on the development of ontologies that provide an organized knowledge system in biomedicine. Particularly, this proposal focused in two objectives: (1) the extraction of specialized terms and (2) the identification of lexical-semantic relationships, in concrete hyponymy/hypernymy and meronymy.