Paper Info
Title
Unsupervised Relation Extraction by Massive Clustering
Abstract
The goal of Information Extraction is to automatically generate structured pieces of information from the relevant information contained in text documents. Machine Learning techniques have been applied to reduce the cost of Information Extraction system adaptation. However, elements of human supervision strongly bias the learning process. Unsupervised learning approaches can avoid these biases. In this paper, we propose an unsupervised approach to learning for Relation Detection, based on the use of massive clustering ensembles. The results obtained on the ACE Relation Mention Detection task outperform in terms of F1 score by 5 points the state of the art of unsupervised techniques for this evaluation framework, in addition to being simpler and more flexible.
Year
DOI
Venue
2009
10.1109/ICDM.2009.81
ICDM
Keywords
Field
DocType
unsupervised relation extraction,information extraction system adaptation,unsupervised learning approach,relevant information,information extraction,f1 score,relation detection,ace relation mention detection,massive clustering,unsupervised technique,machine learning technique,unsupervised approach,probability density function,machine learning,data mining,information retrieval,relation extraction,text analysis,unsupervised learning,covariance matrix
Data mining,Competitive learning,F1 score,Computer science,Automatic Content Extraction,Information extraction,Unsupervised learning,Artificial intelligence,Conceptual clustering,Cluster analysis,Machine learning,Relationship extraction
Conference
ISSN
Citations 
PageRank 
1550-4786
7
0.49
References 
Authors
10
2
Name
Order
Citations
PageRank
Edgar Gonzàlez1394.82
Jordi Turmo230630.52