High-performance unsupervised relation extraction from large corpora

Binjamin Rozenfeld*, Ronen Feldman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations


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 cooccuring 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-of-words 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.

Original languageAmerican English
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Number of pages6
StatePublished - 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference6th International Conference on Data Mining, ICDM 2006
CityHong Kong


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