Similarity metrics within a point of view

Aerts, S; Kitto, K; Sitbon, L

Similarity metrics within a point of view

Aerts, S Kitto, K

Sitbon, L

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7052 LNCS pp. 13 - 24
Issue Date:: 2011-11-02

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Field	Value	Language
dc.contributor.author	Aerts, S	en_US
dc.contributor.author	Kitto, K https://orcid.org/0000-0001-7642-7121	en_US
dc.contributor.author	Sitbon, L	en_US
dc.date.issued	2011-11-02	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7052 LNCS pp. 13 - 24	en_US
dc.identifier.isbn	9783642249709	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119693
dc.description.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. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-24971-6_3	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Similarity metrics within a point of view	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7052 LNCS	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7052 LNCS	en_US

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. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/119693