Difference between revisions of "Measuring Conceptual Similarity by Spreading Activation over Wikipedia's Hyperlink Structure"
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| − | '''Measuring Conceptual Similarity by Spreading Activation over Wikipedia's Hyperlink Structure''' - scientific work related to Wikipedia quality published in 2010, written by Stephan Gouws, G-J van Rooyen and Herman A. Engelbrecht. | + | '''Measuring Conceptual Similarity by Spreading Activation over Wikipedia's Hyperlink Structure''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Stephan Gouws]], [[G-J van Rooyen]] and [[Herman A. Engelbrecht]]. |
== Overview == | == Overview == | ||
| − | Keyword-matching systems based on simple models of semantic relatedness are inadequate at modelling the ambiguities in natural language text, and cannot reliably address the increasingly complex information needs of users. In this paper authors propose novel methods for computing semantic relatedness by spreading activation energy over the hyperlink structure of Wikipedia. Authors demonstrate that techniques can approach state-of-the-art performance, while requiring only a fraction of the background data. | + | Keyword-matching systems based on simple models of semantic [[relatedness]] are inadequate at modelling the ambiguities in natural language text, and cannot reliably address the increasingly complex information needs of users. In this paper authors propose novel methods for computing semantic relatedness by spreading activation energy over the hyperlink structure of [[Wikipedia]]. Authors demonstrate that techniques can approach state-of-the-art performance, while requiring only a fraction of the background data. |
Revision as of 22:45, 9 August 2019
Measuring Conceptual Similarity by Spreading Activation over Wikipedia's Hyperlink Structure - scientific work related to Wikipedia quality published in 2010, written by Stephan Gouws, G-J van Rooyen and Herman A. Engelbrecht.
Overview
Keyword-matching systems based on simple models of semantic relatedness are inadequate at modelling the ambiguities in natural language text, and cannot reliably address the increasingly complex information needs of users. In this paper authors propose novel methods for computing semantic relatedness by spreading activation energy over the hyperlink structure of Wikipedia. Authors demonstrate that techniques can approach state-of-the-art performance, while requiring only a fraction of the background data.
