Terminology-driven mining of biomedical literature

Authors:
Goran Nenadić;Irena Spasić;Sophia Ananiadou
Affiliations:
University of Salford, Salford, UK;University of Salford, Salford, UK;University of Salford, Salford, UK
Venue:
Proceedings of the 2003 ACM symposium on Applied computing
Year:
2003

Citing 5
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Abstract

In this paper we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and structuring performed on a corpus of biomedical abstracts are presented.