DocumentCode
168416
Title
REEL: A Relation Extraction Learning framework
Author
Barrio, P. ; Simoes, G. ; Galhardas, H. ; Gravano, L.
Author_Institution
Columbia Univ., New York, NY, USA
fYear
2014
fDate
8-12 Sept. 2014
Firstpage
455
Lastpage
456
Abstract
We introduce the REEL (RElation Extraction Learning) framework, an open source framework that facilitates the development and evaluation of relation extraction systems over text collections. To define a relation extraction system for a new relation and text collection, users only need to specify the parsers to load the collection, the relation and its constraints, and the learning and extraction techniques to be used. This makes REEL a powerful framework to enable the deployment and evaluation of relation extraction systems for both application building and research.
Keywords
learning (artificial intelligence); public domain software; text analysis; word processing; REEL; extraction technique; learning technique; open source; relation extraction learning; relation extraction systems; text collections; Data mining; Feature extraction; Loading; Logic gates; Natural language processing; Text processing; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Libraries (JCDL), 2014 IEEE/ACM Joint Conference on
Conference_Location
London
Type
conf
DOI
10.1109/JCDL.2014.6970222
Filename
6970222
Link To Document