Treffer: ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.
Title:
ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.
Authors:
Settles B; Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison Madison, WI 52706, USA. bsettles@cs.wisc.edu
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2005 Jul 15; Vol. 21 (14), pp. 3191-2. Date of Electronic Publication: 2005 Apr 28.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, P.H.S.
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1367-4803 (Print) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Algorithms* , Database Management Systems* , Databases, Bibliographic* , Natural Language Processing* , Periodicals as Topic* , Software* , User-Computer Interface*, Information Storage and Retrieval/*methods, Artificial Intelligence ; Genes/genetics ; Programming Languages ; Proteins/classification ; Vocabulary, Controlled
Grant Information:
5T15LM007359 United States LM NLM NIH HHS; R01 LM07050-01 United States LM NLM NIH HHS
Substance Nomenclature:
0 (Proteins)
Entry Date(s):
Date Created: 20050430 Date Completed: 20050927 Latest Revision: 20220318
Update Code:
20250114
DOI:
10.1093/bioinformatics/bti475
PMID:
15860559
Database:
MEDLINE
Weitere Informationen
ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.