PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Directional Distributional Similarity for Lexical Inference
Lili Kotlerman, Ido Dagan, Idan Szpektor and Maayan Geffet
Natural Language Engineering Volume 16, Number 4, pp. 359-389, 2010.

Abstract

Distributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional (asymmetric) similarity measures that aim to quantify distributional feature inclusion. We identify desired properties of such measures for lexical inference, specify a particular measure based on Average Precision that addresses these properties, and demonstrate the empirical benefit of directional measures for two different NLP datasets.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8618
Deposited By:Lili Kotlerman
Deposited On:15 February 2012