Information Retrieval Systems Adapted to the Biomedical Domain
This is an incremental review paper for researchers in biomedical information retrieval, focusing on adapting systems to domain-specific lexical challenges.
The paper reviews techniques like BioNLP, lexical-semantic resources, and BioNER to address high synonymy and homonymy in biomedical terminology for information retrieval, and discusses evaluation methods to assess their suitability.
The terminology used in Biomedicine shows lexical peculiarities 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 the techniques used in this domain, 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.