A comparison study on algorithms of detecting long forms for short forms in biomedical text.
BACKGROUND
With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e., detecting definitions of acronyms, abbreviations, and symbols in biomedical text. We denote acronyms, abbreviations, and symbols as short forms (SFs) and their corresponding definitions as long forms (LFs). The study was designed to answer the following questions; i) how well a system performs in detecting LFs from novel text, ii) what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii) how to combine results from various SF knowledge bases.


METHODS
We evaluated the following three publicly available detection systems in detecting LFs for SFs: i) a handcrafted pattern/rule based system by Ao and Takagi, ALICE, ii) a machine learning system by Chang et al., and iii) a simple alignment-based program by Schwartz and Hearst. In addition, we investigated the conceptual coverage of two terminological knowledge bases: i) the UMLS (the Unified Medical Language System), and ii) the BioThesaurus (a thesaurus of names for all UniProt protein records). We also implemented a web interface that provides a virtual integration of various SF knowledge bases.


RESULTS
We found that detection systems agree with each other on most cases, and the existing terminological knowledge bases have a good coverage of synonymous relationship for frequently defined LFs. The web interface allows people to detect SF definitions from text and to search several SF knowledge bases.


BACKGROUND
The web site is http://gauss.dbb.georgetown.edu/liblab/SFThesaurus.
Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, 4000 Resevoir Rd, NW, Washington, DC 20057, USA. mt352@georgetown.edu
Manabu
M
Torii
Zhang-zhi
ZZ
Hu
Min
M
Song
Cathy H
CH
Wu
Hongfang
H
Liu
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D012660:Semantics; D014584:User-Computer Interface