Difference between revisions of "Word Sense Disambiguation based on Wikipedia Link Structure"
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| − | '''Word Sense Disambiguation based on Wikipedia Link Structure''' - scientific work related to Wikipedia quality published in 2009, written by Angela Fogarolli. | + | '''Word Sense Disambiguation based on Wikipedia Link Structure''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Angela Fogarolli]]. |
== Overview == | == Overview == | ||
| − | In this paper an approach based on Wikipedia link structure for sense disambiguation is presented and evaluated. Wikipedia is used as a reference to obtain lexicographic relationships and in combination with statistical information extraction it is possible to deduce concepts related to the terms extracted from a corpus. In addition, since the corpus covers a representation of a part of the real world the corpus itself is used as ”training data” for choosing the sense which best fit the corpus. | + | In this paper an approach based on [[Wikipedia]] link structure for sense disambiguation is presented and evaluated. Wikipedia is used as a reference to obtain lexicographic relationships and in combination with statistical [[information extraction]] it is possible to deduce concepts related to the terms extracted from a corpus. In addition, since the corpus covers a representation of a part of the real world the corpus itself is used as ”training data” for choosing the sense which best fit the corpus. |
Revision as of 09:43, 1 August 2019
Word Sense Disambiguation based on Wikipedia Link Structure - scientific work related to Wikipedia quality published in 2009, written by Angela Fogarolli.
Overview
In this paper an approach based on Wikipedia link structure for sense disambiguation is presented and evaluated. Wikipedia is used as a reference to obtain lexicographic relationships and in combination with statistical information extraction it is possible to deduce concepts related to the terms extracted from a corpus. In addition, since the corpus covers a representation of a part of the real world the corpus itself is used as ”training data” for choosing the sense which best fit the corpus.
