Difference between revisions of "Wikipedia Sets: Context-Oriented Related Entity Acquisition from Multiple Words"
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| − | '''Wikipedia Sets: Context-Oriented Related Entity Acquisition from Multiple Words''' - scientific work related to Wikipedia quality published in 2011, written by Masumi Shirakawa, Kotaro Nakayama, Takahiro Hara and Shojiro Nishio. | + | '''Wikipedia Sets: Context-Oriented Related Entity Acquisition from Multiple Words''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Masumi Shirakawa]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]]. |
== Overview == | == Overview == | ||
In this paper, authors propose a method which acquires related words (entities) from multiple words by naturally disambiguating their meaning and considering their contexts. In addition, authors introduce a bootstrapping method for improving the coverage of association relations. Experimental result shows that method can acquire related words depending on the contexts of multiple words compared to the ESA-based method. | In this paper, authors propose a method which acquires related words (entities) from multiple words by naturally disambiguating their meaning and considering their contexts. In addition, authors introduce a bootstrapping method for improving the coverage of association relations. Experimental result shows that method can acquire related words depending on the contexts of multiple words compared to the ESA-based method. | ||
Revision as of 09:40, 30 August 2020
Wikipedia Sets: Context-Oriented Related Entity Acquisition from Multiple Words - scientific work related to Wikipedia quality published in 2011, written by Masumi Shirakawa, Kotaro Nakayama, Takahiro Hara and Shojiro Nishio.
Overview
In this paper, authors propose a method which acquires related words (entities) from multiple words by naturally disambiguating their meaning and considering their contexts. In addition, authors introduce a bootstrapping method for improving the coverage of association relations. Experimental result shows that method can acquire related words depending on the contexts of multiple words compared to the ESA-based method.
