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dc.contributor.authorKartsaklis, Den_US
dc.contributor.authorRamgoolam, Sen_US
dc.contributor.authorSadrzadeh, Men_US
dc.date.accessioned2018-04-26T10:33:38Z
dc.date.available2018-03-02en_US
dc.date.submitted2018-01-15T17:13:29.580Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/36517
dc.description32 pages, 3 figuresen_US
dc.description32 pages, 3 figuresen_US
dc.description.abstractRecent research in computational linguistics has developed algorithms which associate matrices with adjectives and verbs, based on the distribution of words in a corpus of text. These matrices are linear operators on a vector space of context words. They are used to construct the meaning of composite expressions from that of the elementary constituents, forming part of a compositional distributional approach to semantics. We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations. A simple Gaussian model is tested against word matrices created from a large corpus of text. We characterize the cubic and quartic departures from the model, which we propose, alongside the Gaussian parameters, as signatures for comparison of linguistic corpora. We propose that perturbed Gaussian models with permutation symmetry provide a promising framework for characterizing the nature of universality in the statistical properties of word matrices. The matrix theory framework developed here exploits the view of statistics as zero dimensional perturbative quantum field theory. It perceives language as a physical system realizing a universality class of matrix statistics characterized by permutation symmetry.en_US
dc.relation.ispartofANNALES DE L’INSTITUT HENRI POINCARÉen_US
dc.subjectcs.CLen_US
dc.subjectcs.CLen_US
dc.subjecthep-then_US
dc.subjectmath.COen_US
dc.titleLinguistic Matrix Theoryen_US
dc.typeArticle
dc.rights.holder© The Author(s) 2018
pubs.author-urlhttp://arxiv.org/abs/1703.10252v1en_US
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2018-03-02en_US


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