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Similarity metrics within a point of view

Journal Contribution - Journal Article

In vector space based approaches to natural language processing,
similarity is commonly measured by taking the angle between
two vectors representing words or documents in a semantic space. This
is natural from a mathematical point of view, as the angle between unit
vectors is, up to constant scaling, the only unitarily invariant metric on
the unit sphere. However, similarity judgement tasks reveal that human
subjects fail to produce data which satisfies the symmetry and triangle
inequality requirements for a metric space. A possible conclusion,
reached in particular by Tversky et al., is that some of the most basic
assumptions of geometric models are unwarranted in the case of psychological
similarity, a result which would impose strong limits on 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 fundamental criticism of of the applicability
of vector space models of cognition. We argue that pairs of words imply
a context which in turn induces a point of view, 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. Hence the triangle inequality ceases to be a valid
constraint on the angles. We test the proposal on a few triples of words
and outline further research.
Journal: Lecture Notes in Computer Science
ISSN: 0302-9743
Pages: 13-24
Number of pages: 12
Publication year:2011
Keywords:Similarity, Semantic Space, triangle inequality, metric, context, povv
  • VABB Id: c:vabb:325583
  • Scopus Id: 80055047321