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Inductive probabilistic taxonomy learning using singular value decomposition

Published online by Cambridge University Press:  05 January 2011

FRANCESCA FALLUCCHI  and
FABIO MASSIMO ZANZOTTO
FRANCESCA FALLUCCHI
Affiliation:
Department of Computer Science, Systems and Production, University of Rome “Tor Vergata”, Italy emails: fallucchi@info.uniroma2.it, zanzotto@info.uniroma2.it
FABIO MASSIMO ZANZOTTO
Affiliation:
Department of Computer Science, Systems and Production, University of Rome “Tor Vergata”, Italy emails: fallucchi@info.uniroma2.it, zanzotto@info.uniroma2.it
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Abstract

Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning, such as networks of words or concepts, are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Learning word meaning from texts is then an active area of research. Lexico-syntactic pattern methods are one of the possible solutions. Yet, these models do not use structural properties of target semantic relations, e.g. transitivity, during learning. In this paper, we propose a novel lexico-syntactic pattern probabilistic method for learning taxonomies that explicitly models transitivity and naturally exploits vector space model techniques for reducing space dimensions. We define two probabilistic models: the direct probabilistic model and the induced probabilistic model. The first is directly estimated on observations over text collections. The second uses transitivity on the direct probabilistic model to induce probabilities of derived events. Within our probabilistic model, we also propose a novel way of using singular value decomposition as unsupervised method for feature selection in estimating direct probabilities. We empirically show that the induced probabilistic taxonomy learning model outperforms state-of-the-art probabilistic models and our unsupervised feature selection method improves performance.

Type
Papers
Information
Natural Language Engineering , Volume 17 , Issue 1 , January 2011 , pp. 71 - 94
Copyright
Copyright © Cambridge University Press 2011

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