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 ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Manabu Torii, Zhang-Zhi Hu, Min Song, Cathy H. Wu, Hongfang Liu
• Keywords: BMCBI 2007, Knowledge Bases, SF Knowledge Bases, Terminological Knowledge Bases