Abstract

The terminology used in biomedicine has lexical characteristics that have required the elaboration of terminological resources and information retrieval systems with specific functionalities. The main characteristics are the high rates of synonymy and homonymy, due to phenomena such as the proliferation of polysemic acronyms and their interaction with common language. Information retrieval systems in the biomedical domain use techniques oriented to the treatment of these lexical peculiarities. In this paper we review some of these techniques, such as the application of Natural Language Processing (BioNLP), the incorporation of lexical-semantic resources, and the application of Named Entity Recognition (BioNER). Finally, we present the evaluation methods adopted to assess the suitability of these techniques for retrieving biomedical resources.

Abstract

This study gives an overview of the process of clustering scientific disciplines using hybrid methods, detecting and labelling emerging topics and analysing the results using bibliometrics methods. The hybrid clustering techniques are based on biblo graphic coupling and text-mining and ‘core documents’, and cross-citation links are used to identify emerging fields. The collaboration network of those countries that proved to be most active in the underlying disciplines, in combination with a set of standard indicators, form the groundwork for the bibliometric analysis of the detected emerging research topics.