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BMCBI
2007

A comparison study on algorithms of detecting long forms for short forms in biomedical text

8 years 7 months ago
A comparison study on algorithms of detecting long forms for short forms in biomedical text
Motivation: 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. Method: 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 ...
Manabu Torii, Zhang-Zhi Hu, Min Song, Cathy H. Wu,
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Manabu Torii, Zhang-Zhi Hu, Min Song, Cathy H. Wu, Hongfang Liu
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