Difference between revisions of "Learning to Compute Semantic Relatedness Using Knowledge from Wikipedia"
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| − | '''Learning to Compute Semantic Relatedness Using Knowledge from Wikipedia''' - scientific work related to Wikipedia quality published in 2014, written by Chen Zheng, Zhichun Wang, Rongfang Bie and Mingquan Zhou. | + | '''Learning to Compute Semantic Relatedness Using Knowledge from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Chen Zheng]], [[Zhichun Wang]], [[Rongfang Bie]] and [[Mingquan Zhou]]. |
== Overview == | == Overview == | ||
| − | Recently, Wikipedia has become a very important resource for computing semantic relatedness (SR) between entities. Several approaches have already been proposed to compute SR based on Wikipedia. Most of the existing approaches use certain kinds of information in Wikipedia (e.g. links, categories, and texts) and compute the SR by empirically designed measures. Authors have observed that these approaches produce very different results for the same entity pair in some cases. Therefore, how to select appropriate features and measures to best approximate the human judgment on SR becomes a challenging problem. In this paper, authors propose a supervised learning approach for computing SR between entities based on Wikipedia. Given two entities, approach first maps entities to articles in Wikipedia; then different kinds of features of the mapped articles are extracted from Wikipedia, which are then combined with different relatedness measures to produce nine raw SR values of the entity pair. A supervised learning algorithm is proposed to learn the optimal weights of different raw SR values. The final SR is computed as the weighted average of raw SRs. Experiments on benchmark datasets show that approach outperforms baseline methods. | + | Recently, [[Wikipedia]] has become a very important resource for computing semantic [[relatedness]] (SR) between entities. Several approaches have already been proposed to compute SR based on Wikipedia. Most of the existing approaches use certain kinds of information in Wikipedia (e.g. links, [[categories]], and texts) and compute the SR by empirically designed [[measures]]. Authors have observed that these approaches produce very different results for the same entity pair in some cases. Therefore, how to select appropriate [[features]] and measures to best approximate the human judgment on SR becomes a challenging problem. In this paper, authors propose a supervised learning approach for computing SR between entities based on Wikipedia. Given two entities, approach first maps entities to articles in Wikipedia; then different kinds of features of the mapped articles are extracted from Wikipedia, which are then combined with different relatedness measures to produce nine raw SR values of the entity pair. A supervised learning algorithm is proposed to learn the optimal weights of different raw SR values. The final SR is computed as the weighted average of raw SRs. Experiments on benchmark datasets show that approach outperforms baseline methods. |
Revision as of 07:29, 1 September 2019
Learning to Compute Semantic Relatedness Using Knowledge from Wikipedia - scientific work related to Wikipedia quality published in 2014, written by Chen Zheng, Zhichun Wang, Rongfang Bie and Mingquan Zhou.
Overview
Recently, Wikipedia has become a very important resource for computing semantic relatedness (SR) between entities. Several approaches have already been proposed to compute SR based on Wikipedia. Most of the existing approaches use certain kinds of information in Wikipedia (e.g. links, categories, and texts) and compute the SR by empirically designed measures. Authors have observed that these approaches produce very different results for the same entity pair in some cases. Therefore, how to select appropriate features and measures to best approximate the human judgment on SR becomes a challenging problem. In this paper, authors propose a supervised learning approach for computing SR between entities based on Wikipedia. Given two entities, approach first maps entities to articles in Wikipedia; then different kinds of features of the mapped articles are extracted from Wikipedia, which are then combined with different relatedness measures to produce nine raw SR values of the entity pair. A supervised learning algorithm is proposed to learn the optimal weights of different raw SR values. The final SR is computed as the weighted average of raw SRs. Experiments on benchmark datasets show that approach outperforms baseline methods.
