Similarity metrics within a point of view

Authors:
Sven Aerts;Kirsty Kitto;Laurianne Sitbon
Affiliations:
Centre for Interdisciplinary Studies CLEA, Vrije Universiteit Brussel;Faculty of Science and Technology, Queensland University of Technology;Faculty of Science and Technology, Queensland University of Technology
Venue:
QI'11 Proceedings of the 5th international conference on Quantum interaction
Year:
2011

Citing 3
Cited 0

A vector space model for automatic indexing

Communications of the ACM
Automatic word sense discrimination

Computational Linguistics - Special issue on word sense disambiguation
From frequency to meaning: vector space models of semantics

Journal of Artificial Intelligence Research

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Abstract

Vector space based approaches to natural language processing are contrasted with human similarity judgements to show the manner in which human subjects fail to produce data which satisfies all requirements for a metric space. This result would constrains the validity and applicability vector space based (and hence also quantum inspired) approaches to the modelling of cognitive processes. This paper proposes a resolution to this problem, by arguing that pairs of words imply a context which in turn induces a point of view, so allowing a subject to estimate semantic similarity. Context is here introduced as a point of view vector (POVV) and the expected similarity is derived as a measure over the POVV's. Different pairs of words will invoke different contexts and different POVV's. We illustrate the proposal on a few triples of words and outline further research.