DocumentCode :
464193
Title :
Learning Relations and Information Extraction Rules for Protein Annotation
Author :
Kim, Jee-Hyub ; Artificial, M.H.
Author_Institution :
Artificial Intell. Lab., Univ. of Geneva, Geneva
Volume :
1
fYear :
2007
fDate :
21-23 May 2007
Firstpage :
349
Lastpage :
354
Abstract :
Protein annotation is a task that describes protein X in terms of topic Y Until now, most of protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever rapidly, manual annotation becomes difficult, and there is increasing need to automate the protein annotation process. Recently, Information Extraction (IE) has been used to solve this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real world domains such as the biomedical domain. In this paper, we describe an IE system which requires only sentences labeled relevant or not to a given topic by domain experts.
Keywords :
biology computing; knowledge acquisition; proteins; human annotators; information extraction rules; manual annotation; protein annotation process; Artificial intelligence; Biomedical engineering; Data mining; Databases; Humans; Knowledge engineering; Laboratories; Learning; Protein engineering; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on
Conference_Location :
Niagara Falls, Ont.
Print_ISBN :
978-0-7695-2847-2
Type :
conf
DOI :
10.1109/AINAW.2007.220
Filename :
4221084
Link To Document :
بازگشت