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Directional distributional similarity for lexical inference

Published online by Cambridge University Press:  11 October 2010

LILI KOTLERMAN ,
IDO DAGAN ,
IDAN SZPEKTOR  and
MAAYAN ZHITOMIRSKY-GEFFET
LILI KOTLERMAN
Affiliation:
Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel e-mail: lili.dav@gmail.com
IDO DAGAN
Affiliation:
Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel e-mail: dagan@cs.biu.ac.il
IDAN SZPEKTOR
Affiliation:
Yahoo! Research, Building 30 Matam Park, Haifa 31905, Israel e-mail: idan@yahoo-inc.com
MAAYAN ZHITOMIRSKY-GEFFET
Affiliation:
Department of Information Science, Bar-Ilan University, Ramat Gan, Israel e-mail: maayan.geffet@gmail.com
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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.

Type
Papers
Information
Natural Language Engineering , Volume 16 , Special Issue 4: Distributional Lexical Semantics , October 2010 , pp. 359 - 389
Copyright
Copyright © Cambridge University Press 2010

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