Generalized vector space model: Difference between revisions

Revision as of 15:19, 7 December 2015

Revision as of 15:27, 3 April 2016

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{{Confusing|date=January 2010}}

The '''Generalized vector space model''' is a generalization of the [[vector space model]] used in [[information retrieval]]. Wong ''et al.''<ref name="wong">{{cite | title=Generalized vector spaces model in information retrieval | url=http://doi.acm.org/10.1145/253495.253506 | firstfirst1=S. K. M. | lastlast1=Wong | coauthorsfirst2=Wojciech |last2=Ziarko, |first3=Patrick C. N. |last3=Wong | publisher=[[Association for Computing Machinery|SIGIR ACM]] | date=1985-06-05}}</ref> presented an analysis of the problems that the pairwise orthogonality assumption of the [[vector space model]] (VSM) creates. From here they extended the VSM to the generalized vector space model (GVSM).

==Definitions==

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# compute frequency co-occurrence statistics from large corpora

Recently Tsatsaronis<ref>{{cite | title=A Generalized Vector Space Model for Text Retrieval Based on Semantic Relatedness | url=http://www.aclweb.org/anthology/E/E09/E09-3009.pdf | lastlast1= Tsatsaronis | firstfirst1=George | coauthorsfirst2=Vicky | last2=Panagiotopoulou | publisher=[[Association for Computing Machinery|EACL ACM]] |date=2009-04-02}}</ref> focused on the first approach.

They measure semantic relatedness (''SR'') using a thesaurus (''O'') like [[WordNet]]. It considers the path length, captured by compactness (''SCM''), and the path depth, captured by semantic path elaboration (''SPE'').