Difference between revisions of "Extraction of Semantic Relations Between Concepts with Knn Algorithms on Wikipedia"
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| − | '''Extraction of Semantic Relations Between Concepts with Knn Algorithms on Wikipedia''' - scientific work related to Wikipedia quality published in 2012, written by Alexander Panchenko and Cédrick Fairon. | + | '''Extraction of Semantic Relations Between Concepts with Knn Algorithms on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Alexander Panchenko]] and [[Cédrick Fairon]]. |
== Overview == | == Overview == | ||
| − | This paper presents methods for extraction of semantic relations be- tween words. The methods rely on the k-nearest neighbor algorithms and two semantic similarity measures to extract relations from the abstracts of Wikipe- dia articles. Authors analyze the proposed methods and evaluate their performance. Precision of the extraction with the best method achieves 83%. Authors also present an open source system which effectively implements the described algorithms. | + | This paper presents methods for extraction of semantic relations be- tween words. The methods rely on the k-nearest neighbor algorithms and two [[semantic similarity]] [[measures]] to extract relations from the abstracts of Wikipe- dia articles. Authors analyze the proposed methods and evaluate their performance. Precision of the extraction with the best method achieves 83%. Authors also present an [[open source]] system which effectively implements the described algorithms. |
Revision as of 23:34, 30 May 2019
Extraction of Semantic Relations Between Concepts with Knn Algorithms on Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Alexander Panchenko and Cédrick Fairon.
Overview
This paper presents methods for extraction of semantic relations be- tween words. The methods rely on the k-nearest neighbor algorithms and two semantic similarity measures to extract relations from the abstracts of Wikipe- dia articles. Authors analyze the proposed methods and evaluate their performance. Precision of the extraction with the best method achieves 83%. Authors also present an open source system which effectively implements the described algorithms.
