Terminology-driven mining of biomedical literature.
BACKGROUND
With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing.


RESULTS
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 clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively.


BACKGROUND
software for the terminology management is available upon request.
Nenadic
Goran
G
Computer Science, University of Salford, Salford M5 4WT, UK. g.nenadic@umist.ac.uk
Spasic
Irena
I
Ananiadou
Sophia
S
D000004:Abbreviations as Topic; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D009626:Terminology as Topic; D018875:Vocabulary, Controlled