Directional distributional similarity for lexical inference
- 1 January 2010
- journal article
- research article
- Published by Cambridge University Press (CUP) in Natural Language Engineering
- Vol. 16 (4), 359-389
- https://doi.org/10.1017/s1351324910000124
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.Keywords
This publication has 21 references indexed in Scilit:
- Satisfying information needs with multi-document summariesInformation Processing & Management, 2007
- Representing word meaning and order information in a composite holographic lexicon.Psychological Review, 2007
- Evaluating WordNet-based Measures of Lexical Semantic RelatednessComputational Linguistics, 2006
- Reformulation of queries using similarity thesauriInformation Processing & Management, 2005
- Learning with unlabeled data for text categorization using bootstrapping and feature projection techniquesPublished by Association for Computational Linguistics (ACL) ,2004
- A corpus analysis approach for automatic query expansion and its extension to multiple databasesACM Transactions on Information Systems, 1999
- Measures of distributional similarityPublished by Association for Computational Linguistics (ACL) ,1999
- Experiments on linguistically-based term associationsInformation Processing & Management, 1992
- Features of similarity.Psychological Review, 1977
- Individual Comparisons by Ranking MethodsBiometrics Bulletin, 1945