Abstract

In recent years, the automatic processing of biomedical information has been benefited from advances made by data and text mining. An example of this advance is the book edited by Ananiadou & McNaught (2006), who give a special relevance to create and use tools capable to extract information from large collections of documents, particularly PubMed (www.ncbi.nlm.nih.gov/pubmed/). In fact, given this dimension, Pubmed is the most important repository for obtaining biomedical data, what has motivated the generation of different projects related to computational linguistics such as the Corpus Genia (www.geniaproject.org), the MEDIE search engine (www.nactem.ac.uk/medie/), or the Open Biological and Biomedical Ontology Project (http://obofoundry.org/), focused on the development of ontologies that provide an organized knowledge system in biomedicine. In line with these projects, it is exposed here a method for performing a concept mining on biomedical documents in Spanish. This model is based on the automatic extraction of definitional contexts (or DCs), according to the framework developed by Sierra et al. (2008), Sierra (2009), Aguilar & Acosta (2016), as well as Aguilar et al. (2016). Our model has been sketched having in mind the following objectives: • The linguistic analysis of definitional contexts identified in biomedical texts in Spanish. • The creation of a linguistic corpus in Spanish that is representative for the biomedical area. • The use of stochastic methods in order to provide empirical evidence to validate in linguistic analysis previously performed. These objectives allow to establish a set of concrete tasks that can be implemented as modules for a mining system, concretely: a) Extraction of terminological information: this one focuses on establishing a chain of text processing, which considers: (i) the selection, tokenization and syntactic annotation of a Spanish corpus in medicine; (ii) the identification of uni/multiword terms using a hybrid method (Acosta, Aguilar & Infante, 2015). b) Identification of lexical relations: taking advantage of the term extraction and linking them to their definitions, it is possible to implement an ontology in Spanish, considering the recognition of lexical-semantic relations, specifically hyponymy-hyperonymy and meronymy, based on the proposal of Arp, Smith & Spear (2015). The organization of this article is as follows: section 1 shows a general background behind the notion of concept mining; the section 2 describes how is performed the identification of DCs, in order to detect and extract terms that function as hyponyms, hypernyms and meronyms. The section 3 exposes the results of these extractions, delineating a possible ontology that organizes such lexical-semantic information. To conclude, the section 4 offers a summary along with a brief discussion respect to future applications of this model of mining concepts.