PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Entailment Rules for Unary Templates
Idan Szpektor and Ido Dagan
In: COLING 2008, 18-22 Aug 2008, Manchester, England.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:4483
Deposited By:Idan Szpektor
Deposited On:13 March 2009