Difference between revisions of "Relation Extraction from Wikipedia Leveraging Intrinsic Patterns"
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+ | {{Infobox work | ||
+ | | title = Relation Extraction from Wikipedia Leveraging Intrinsic Patterns | ||
+ | | date = 2015 | ||
+ | | authors = [[Yulong Gu]]<br />[[Weidong Liu]]<br />[[Jiaxing Song]] | ||
+ | | doi = 10.1109/WI-IAT.2015.175 | ||
+ | | link = http://ieeexplore.ieee.org/document/7396801/ | ||
+ | }} | ||
'''Relation Extraction from Wikipedia Leveraging Intrinsic Patterns''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Yulong Gu]], [[Weidong Liu]] and [[Jiaxing Song]]. | '''Relation Extraction from Wikipedia Leveraging Intrinsic Patterns''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Yulong Gu]], [[Weidong Liu]] and [[Jiaxing Song]]. | ||
== 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 leveraging Intrinsic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 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 leveraging Intrinsic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena significantly outperforms existing methods. |
Revision as of 10:39, 3 February 2021
Authors | Yulong Gu Weidong Liu Jiaxing Song |
---|---|
Publication date | 2015 |
DOI | 10.1109/WI-IAT.2015.175 |
Links | Original |
Relation Extraction from Wikipedia Leveraging Intrinsic Patterns - scientific work related to Wikipedia quality published in 2015, written by Yulong Gu, Weidong Liu and Jiaxing Song.
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 leveraging Intrinsic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena significantly outperforms existing methods.