Learning entailment rules for unary templates

Authors:
Idan Szpektor;Ido Dagan
Affiliations:
Bar-Ilan University, Ramat Gan, Israel;Bar-Ilan University, Ramat Gan, Israel
Venue:
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Year:
2008

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Abstract

Most work on unsupervised entailment rule acquisition focused on rules between templates with two variables, ignoring unary rules - entailment rules between templates with a single variable. In this paper we investigate two approaches for unsupervised learning of such rules and compare the proposed methods with a binary rule learning method. The results show that the learned unary rule-sets outperform the binary rule-set. In addition, a novel directional similarity measure for learning entailment, termed Balanced-Inclusion, is the best performing measure.