Assessing the impact of case sensitivity and term information gain on biomedical concept recognition

PloS One
Tudor Groza, Karin Verspoor

Abstract

Concept recognition (CR) is a foundational task in the biomedical domain. It supports the important process of transforming unstructured resources into structured knowledge. To date, several CR approaches have been proposed, most of which focus on a particular set of biomedical ontologies. Their underlying mechanisms vary from shallow natural language processing and dictionary lookup to specialized machine learning modules. However, no prior approach considers the case sensitivity characteristics and the term distribution of the underlying ontology on the CR process. This article proposes a framework that models the CR process as an information retrieval task in which both case sensitivity and the information gain associated with tokens in lexical representations (e.g., term labels, synonyms) are central components of a strategy for generating term variants. The case sensitivity of a given ontology is assessed based on the distribution of so-called case sensitive tokens in its terms, while information gain is modelled using a combination of divergence from randomness and mutual information. An extensive evaluation has been carried out using the CRAFT corpus. Experimental results show that case sensitivity awareness leads to an ...Continue Reading

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Citations

Nov 11, 2017·Journal of Biomedical Semantics·Marco BasaldellaFabio Rinaldi
Oct 31, 2018·Database : the Journal of Biological Databases and Curation·Chia-Jung Yang, Jung-Hsien Chiang
Jan 1, 2017·Database : the Journal of Biological Databases and Curation·Michael BadaLawrence E Hunter
Sep 11, 2016·Journal of Biomedical Semantics·Christopher S FunkKarin M Verspoor

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Software Mentioned

BioCreative IV
Neji
Jochem
RxNORM
GENIA
NCBO
GDep
MetaMap
SNOMED CT
Gimli

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