Title :
REEL: A Relation Extraction Learning framework
Author :
Barrio, P. ; Simoes, G. ; Galhardas, H. ; Gravano, L.
Author_Institution :
Columbia Univ., New York, NY, USA
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;
Conference_Titel :
Digital Libraries (JCDL), 2014 IEEE/ACM Joint Conference on
Conference_Location :
London
DOI :
10.1109/JCDL.2014.6970222