Difference between revisions of "Exploiting Wikipedia for Entity Name Disambiguation in Tweets"
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== Overview == | == Overview == | ||
Social media repositories serve as a significant source of evidence when extracting information related to the [[reputation]] of a particular entity (e.g., a particular politician, singer or company). Reputation management experts are in need of automated methods for mining the social media repositories (in particular [[Twitter]]) to monitor the reputation of a particular entity. A quite significant research challenge related to the above issue is to disambiguate tweets with respect to entity names. To address this issue in this paper authors use “context phrases” in a tweet and [[Wikipedia]] disambiguated articles for a particular entity in a random forest classifier. Furthermore, authors also utilize the concept of “[[relatedness]]” between tweet and entity using the Wikipedia category-article structure that captures the amount of discussion present inside a tweet related to an entity. The experimental evaluations show a significant improvement over the baseline and comparable performance with other systems representing strong performance given that authors restrict ourselves to [[features]] extracted from Wikipedia. | Social media repositories serve as a significant source of evidence when extracting information related to the [[reputation]] of a particular entity (e.g., a particular politician, singer or company). Reputation management experts are in need of automated methods for mining the social media repositories (in particular [[Twitter]]) to monitor the reputation of a particular entity. A quite significant research challenge related to the above issue is to disambiguate tweets with respect to entity names. To address this issue in this paper authors use “context phrases” in a tweet and [[Wikipedia]] disambiguated articles for a particular entity in a random forest classifier. Furthermore, authors also utilize the concept of “[[relatedness]]” between tweet and entity using the Wikipedia category-article structure that captures the amount of discussion present inside a tweet related to an entity. The experimental evaluations show a significant improvement over the baseline and comparable performance with other systems representing strong performance given that authors restrict ourselves to [[features]] extracted from Wikipedia. | ||
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+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Qureshi, Muhammad Atif; Qureshi, Muhammad Atif; O’Riordan, Colm; Pasi, Gabriella. (2014). "[[Exploiting Wikipedia for Entity Name Disambiguation in Tweets]]". Springer, Cham. DOI: 10.1007/978-3-319-07983-7_25. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Qureshi |first1=Muhammad Atif |last2=Qureshi |first2=Muhammad Atif |last3=O’Riordan |first3=Colm |last4=Pasi |first4=Gabriella |title=Exploiting Wikipedia for Entity Name Disambiguation in Tweets |date=2014 |doi=10.1007/978-3-319-07983-7_25 |url=https://wikipediaquality.com/wiki/Exploiting_Wikipedia_for_Entity_Name_Disambiguation_in_Tweets |journal=Springer, Cham}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Qureshi, Muhammad Atif; Qureshi, Muhammad Atif; O’Riordan, Colm; Pasi, Gabriella. (2014). &quot;<a href="https://wikipediaquality.com/wiki/Exploiting_Wikipedia_for_Entity_Name_Disambiguation_in_Tweets">Exploiting Wikipedia for Entity Name Disambiguation in Tweets</a>&quot;. Springer, Cham. DOI: 10.1007/978-3-319-07983-7_25. | ||
+ | </nowiki> | ||
+ | </code> |
Revision as of 08:18, 28 January 2021
Authors | Muhammad Atif Qureshi Muhammad Atif Qureshi Colm O’Riordan Gabriella Pasi |
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Publication date | 2014 |
DOI | 10.1007/978-3-319-07983-7_25 |
Links | Original |
Exploiting Wikipedia for Entity Name Disambiguation in Tweets - scientific work related to Wikipedia quality published in 2014, written by Muhammad Atif Qureshi, Muhammad Atif Qureshi, Colm O’Riordan and Gabriella Pasi.
Overview
Social media repositories serve as a significant source of evidence when extracting information related to the reputation of a particular entity (e.g., a particular politician, singer or company). Reputation management experts are in need of automated methods for mining the social media repositories (in particular Twitter) to monitor the reputation of a particular entity. A quite significant research challenge related to the above issue is to disambiguate tweets with respect to entity names. To address this issue in this paper authors use “context phrases” in a tweet and Wikipedia disambiguated articles for a particular entity in a random forest classifier. Furthermore, authors also utilize the concept of “relatedness” between tweet and entity using the Wikipedia category-article structure that captures the amount of discussion present inside a tweet related to an entity. The experimental evaluations show a significant improvement over the baseline and comparable performance with other systems representing strong performance given that authors restrict ourselves to features extracted from Wikipedia.
Embed
Wikipedia Quality
Qureshi, Muhammad Atif; Qureshi, Muhammad Atif; O’Riordan, Colm; Pasi, Gabriella. (2014). "[[Exploiting Wikipedia for Entity Name Disambiguation in Tweets]]". Springer, Cham. DOI: 10.1007/978-3-319-07983-7_25.
English Wikipedia
{{cite journal |last1=Qureshi |first1=Muhammad Atif |last2=Qureshi |first2=Muhammad Atif |last3=O’Riordan |first3=Colm |last4=Pasi |first4=Gabriella |title=Exploiting Wikipedia for Entity Name Disambiguation in Tweets |date=2014 |doi=10.1007/978-3-319-07983-7_25 |url=https://wikipediaquality.com/wiki/Exploiting_Wikipedia_for_Entity_Name_Disambiguation_in_Tweets |journal=Springer, Cham}}
HTML
Qureshi, Muhammad Atif; Qureshi, Muhammad Atif; O’Riordan, Colm; Pasi, Gabriella. (2014). "<a href="https://wikipediaquality.com/wiki/Exploiting_Wikipedia_for_Entity_Name_Disambiguation_in_Tweets">Exploiting Wikipedia for Entity Name Disambiguation in Tweets</a>". Springer, Cham. DOI: 10.1007/978-3-319-07983-7_25.