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Automatic discovery of word semantic relations using paraphrase alignment and distributional lexical semantics analysis

Published online by Cambridge University Press:  11 October 2010

GAËL DIAS ,
RUMEN MORALIYSKI ,
JOÃO CORDEIRO ,
ANTOINE DOUCET  and
HELENA AHONEN-MYKA
GAËL DIAS
Affiliation:
Centre for HLT and Bioinformatics, Department of Computer Science, University of Beira Interior, 6201-001 - Covilhã, Portugal emails: ddg@di.ubi.pt, rumen@penhas.di.ubi.pt, jpaulo@di.ubi.pt
RUMEN MORALIYSKI
Affiliation:
Centre for HLT and Bioinformatics, Department of Computer Science, University of Beira Interior, 6201-001 - Covilhã, Portugal emails: ddg@di.ubi.pt, rumen@penhas.di.ubi.pt, jpaulo@di.ubi.pt
JOÃO CORDEIRO
Affiliation:
Centre for HLT and Bioinformatics, Department of Computer Science, University of Beira Interior, 6201-001 - Covilhã, Portugal emails: ddg@di.ubi.pt, rumen@penhas.di.ubi.pt, jpaulo@di.ubi.pt
ANTOINE DOUCET
Affiliation:
Campus Côte de Nacre, Boulevard du Maréchal Juin, University of Caen, BP 5186 - 14032 - Caen CEDEX, France email: doucet@info.unicaen.fr
HELENA AHONEN-MYKA
Affiliation:
Department of Computer Science, University of Helsinki, P.O. Box 68 (Gustaf Hällströmin katu 2b), FI-00014, Helsinki, Finland email: helena.ahonen-myka@cs.helsinki.fi
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Abstract

Thesauri, which list the most salient semantic relations between words, have mostly been compiled manually. Therefore, the inclusion of an entry depends on the subjective decision of the lexicographer. As a consequence, those resources are usually incomplete. In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal differs from all other research presented so far as it tries to take the best of two different methodologies, i.e. semantic space models and information extraction models. In particular, it can be applied to extract close semantic relations, it limits the search space to few, highly probable options and it is unsupervised.

Type
Papers
Information
Natural Language Engineering , Volume 16 , Special Issue 4: Distributional Lexical Semantics , October 2010 , pp. 439 - 467
Copyright
Copyright © Cambridge University Press 2010

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