Improving Relation Extraction by Exploiting Properties of the Target Relation

Authors:
Eric Normand;Kevin Grant;Elias Ioup;John Sample
Affiliations:
Naval Research Laboratory, Stennis Space Center, MS,;Naval Research Laboratory, Stennis Space Center, MS,;Naval Research Laboratory, Stennis Space Center, MS,;Naval Research Laboratory, Stennis Space Center, MS,
Venue:
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Year:
2009

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Abstract

In this paper we demonstrate and quantify the advantage gained by allowing relation extraction algorithms to make use of information about the cardinality of the target relation. The two algorithms presented herein differ only in their assumption about the nature of the target relation (one-to-many or many-to-many). The algorithms are tested on the same relation to show the degree of advantage gained by their differing assumptions. Comparison of the performance of the two algorithms on a one-to-many domain demonstrates the existence of several, previously undocumented behaviors which can be used to improve the performance of relation extraction algorithms. The first is a distinct, inverted u-shape in the initial portion of the recall curve of the many-to-many algorithm. The second is that, as the number of seeds increases, the rate of improvement of the two algorithms descreases to approach the rate at which new information is added via the seeds.