Difference between revisions of "Towards Accurate Relation Extraction from Wikipedia"

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{{Infobox work
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| title = Towards Accurate Relation Extraction from Wikipedia
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| date = 2016
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| authors = [[Yulong Gu]]<br />[[Jiaxing Song]]<br />[[Weidong Liu]]<br />[[Yuan Yao]]<br />[[Lixin Zou]]
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| doi = 10.1109/WI.2016.0023
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| link =
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}}
 
'''Towards Accurate Relation Extraction from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Yulong Gu]], [[Jiaxing Song]], [[Weidong Liu]], [[Yuan Yao]] and [[Lixin Zou]].
 
'''Towards Accurate Relation Extraction from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Yulong Gu]], [[Jiaxing Song]], [[Weidong Liu]], [[Yuan Yao]] and [[Lixin Zou]].
  
 
== Overview ==
 
== Overview ==
 
Enormous efforts of human volunteers have made [[Wikipedia]] become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, authors propose a novel framework called Athena 2.0 leveraging Semantic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 2.0 significantly outperforms existing methods.
 
Enormous efforts of human volunteers have made [[Wikipedia]] become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, authors propose a novel framework called Athena 2.0 leveraging Semantic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 2.0 significantly outperforms existing methods.

Revision as of 05:46, 21 February 2021


Towards Accurate Relation Extraction from Wikipedia
Authors
Yulong Gu
Jiaxing Song
Weidong Liu
Yuan Yao
Lixin Zou
Publication date
2016
DOI
10.1109/WI.2016.0023
Links

Towards Accurate Relation Extraction from Wikipedia - scientific work related to Wikipedia quality published in 2016, written by Yulong Gu, Jiaxing Song, Weidong Liu, Yuan Yao and Lixin Zou.

Overview

Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, authors propose a novel framework called Athena 2.0 leveraging Semantic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 2.0 significantly outperforms existing methods.