DocumentCode :
3106963
Title :
High-Performance Unsupervised Relation Extraction from Large Corpora
Author :
Rozenfeld, Binjamin ; Feldman, Ronen
Author_Institution :
Bar-Ilan Univ., Ramat-Gan
fYear :
2006
fDate :
18-22 Dec. 2006
Firstpage :
1032
Lastpage :
1037
Abstract :
We present URIES - an unsupervised relation identification and extraction system. The system automatically identifies interesting binary relations between entities in the input corpus, and then proceeds to extract a large number of instances of these relations. The system discovers relations by clustering frequently co- occuring pairs of entities, based on the contexts in which they appear. Its complex pattern-based representation of the contexts allows the clustering step to achieve very high precision, sufficient for the clusters to perform as sets of seeds for bootstrapping a high-recall relation extraction process. In a series of experiments we demonstrate the successful performance of URIES and compare it to the two existing systems - a weakly supervised high-recall Web relation extraction system called SRES, and an unsupervised relation identification system that uses a simpler bag-ofwords representation of contexts. The experiments show that URIES performs comparably to SRES, but without any supervision, and that such performance is due to the power of its complex contexts representation and to its novel candidate selection method.
Keywords :
Internet; knowledge acquisition; unsupervised learning; Web relation extraction system; bag-of-words representation; pattern-based representation; unsupervised relation extraction; unsupervised relation identification; Data mining; Gallium nitride; Humans; Knowledge engineering; Machine learning; Relays; Strontium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2006. ICDM '06. Sixth International Conference on
Conference_Location :
Hong Kong
ISSN :
1550-4786
Print_ISBN :
0-7695-2701-7
Type :
conf
DOI :
10.1109/ICDM.2006.82
Filename :
4053148
Link To Document :
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