Difference between revisions of "Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation"
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| + | | title = Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation | ||
| + | | date = 2012 | ||
| + | | authors = [[Shahida Jabeen]]<br />[[Xiaoying Gao]]<br />[[Peter Andreae]] | ||
| + | | doi = 10.4236/jsea.2012.512B034 | ||
| + | | link = http://file.scirp.org/Html/27147.html | ||
| + | }} | ||
'''Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Shahida Jabeen]], [[Xiaoying Gao]] and [[Peter Andreae]]. | '''Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Shahida Jabeen]], [[Xiaoying Gao]] and [[Peter Andreae]]. | ||
== Overview == | == Overview == | ||
Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using [[Wikipedia]] as the external knowledge source to decipher the true meaning of each term using a single term as the context. Authors experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. Authors also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%. | Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using [[Wikipedia]] as the external knowledge source to decipher the true meaning of each term using a single term as the context. Authors experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. Authors also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%. | ||
Revision as of 14:15, 23 November 2019
Authors | Shahida Jabeen Xiaoying Gao Peter Andreae |
|---|---|
Publication date | 2012 |
DOI | 10.4236/jsea.2012.512B034 |
Links | Original |
Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation - scientific work related to Wikipedia quality published in 2012, written by Shahida Jabeen, Xiaoying Gao and Peter Andreae.
Overview
Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using Wikipedia as the external knowledge source to decipher the true meaning of each term using a single term as the context. Authors experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. Authors also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%.
