Difference between revisions of "Typing Software Articles with Wikipedia Category Structure"

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'''Typing Software Articles with Wikipedia Category Structure''' - scientific work related to Wikipedia quality published in 2010, written by Liang Xu, Hideaki Takeda, Masahiro Hamasaki and Huayu Wu.
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'''Typing Software Articles with Wikipedia Category Structure''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Liang Xu]], [[Hideaki Takeda]], [[Masahiro Hamasaki]] and [[Huayu Wu]].
  
 
== Overview ==
 
== Overview ==
In this paper authors present a low-cost method for typing Named Entities with Wikipedia. Different from other text analysis-based approaches, approach relies only on the structural features of Wikipidia and the use of external linguistic resources is optional. Authors perform binary classification of an article by analyzing of the names of its categories as well as the structure. The evaluation shows method can be successfully applied to the ‘software’ category (F 80%).
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In this paper authors present a low-cost method for typing Named Entities with [[Wikipedia]]. Different from other text analysis-based approaches, approach relies only on the structural [[features]] of Wikipidia and the use of external linguistic resources is optional. Authors perform binary classification of an article by analyzing of the names of its [[categories]] as well as the structure. The evaluation shows method can be successfully applied to the ‘software’ category (F 80%).

Revision as of 21:24, 2 February 2021

Typing Software Articles with Wikipedia Category Structure - scientific work related to Wikipedia quality published in 2010, written by Liang Xu, Hideaki Takeda, Masahiro Hamasaki and Huayu Wu.

Overview

In this paper authors present a low-cost method for typing Named Entities with Wikipedia. Different from other text analysis-based approaches, approach relies only on the structural features of Wikipidia and the use of external linguistic resources is optional. Authors perform binary classification of an article by analyzing of the names of its categories as well as the structure. The evaluation shows method can be successfully applied to the ‘software’ category (F 80%).