Difference between revisions of "Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents"
(Overview: Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents) |
(Links) |
||
| Line 1: | Line 1: | ||
| − | '''Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents''' - scientific work related to Wikipedia quality published in 2013, written by Ozge Yeloglu, Evangelos E. Milios and A. Nur Zincir-Heywood. | + | '''Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ozge Yeloglu]], [[Evangelos E. Milios]] and [[A. Nur Zincir-Heywood]]. |
== Overview == | == Overview == | ||
| − | Authors propose a model for assigning Wikipedia Concepts as scientific category labels to scientific documents where their terms are first grouped together using the well-known topic modelling method, Latent Dirichlet Allocation (LDA) and then assigned to Wikipedia Concepts by wikification. Authors wikify the terms of the topic model of a document to extract related concepts from Wikipedia. Authors experiment on two different datasets: the abstracts of the documents from the ACM Digital Library and the full papers of the UvT Collection. The ACM dataset includes Computer Science publications whereas UvT includes scientific publications from a range of topics. Domain specific taxonomies are used for evaluation. Results show that approach is able to assign Wikipedia Concepts to the scientific publications in an automated manner, removing any need for human supervision. | + | Authors propose a model for assigning [[Wikipedia]] Concepts as scientific category labels to scientific documents where their terms are first grouped together using the well-known topic modelling method, Latent Dirichlet Allocation (LDA) and then assigned to Wikipedia Concepts by wikification. Authors wikify the terms of the topic model of a document to extract related concepts from Wikipedia. Authors experiment on two different datasets: the abstracts of the documents from the ACM Digital Library and the full papers of the UvT Collection. The ACM dataset includes Computer Science publications whereas UvT includes scientific publications from a range of topics. Domain specific taxonomies are used for evaluation. Results show that approach is able to assign Wikipedia Concepts to the scientific publications in an automated manner, removing any need for human supervision. |
Revision as of 10:54, 27 February 2021
Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents - scientific work related to Wikipedia quality published in 2013, written by Ozge Yeloglu, Evangelos E. Milios and A. Nur Zincir-Heywood.
Overview
Authors propose a model for assigning Wikipedia Concepts as scientific category labels to scientific documents where their terms are first grouped together using the well-known topic modelling method, Latent Dirichlet Allocation (LDA) and then assigned to Wikipedia Concepts by wikification. Authors wikify the terms of the topic model of a document to extract related concepts from Wikipedia. Authors experiment on two different datasets: the abstracts of the documents from the ACM Digital Library and the full papers of the UvT Collection. The ACM dataset includes Computer Science publications whereas UvT includes scientific publications from a range of topics. Domain specific taxonomies are used for evaluation. Results show that approach is able to assign Wikipedia Concepts to the scientific publications in an automated manner, removing any need for human supervision.
