Difference between revisions of "A Spousal Relation Begins with a Deletion of Engage and Ends with an Addition of Divorce: Learning State Changing Verbs from Wikipedia Revision History"

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{{Infobox work
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| title = A Spousal Relation Begins with a Deletion of Engage and Ends with an Addition of Divorce: Learning State Changing Verbs from Wikipedia Revision History
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| date = 2015
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| authors = [[Derry Tanti Wijaya]]<br />[[Ndapandula Nakashole]]<br />[[Tom M. Mitchell]]
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| doi = 10.18653/v1/D15-1059
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| link = http://www.aclweb.org/anthology/D15-1059
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}}
 
'''A Spousal Relation Begins with a Deletion of Engage and Ends with an Addition of Divorce: Learning State Changing Verbs from Wikipedia Revision History''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Derry Tanti Wijaya]], [[Ndapandula Nakashole]] and [[Tom M. Mitchell]].
 
'''A Spousal Relation Begins with a Deletion of Engage and Ends with an Addition of Divorce: Learning State Changing Verbs from Wikipedia Revision History''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Derry Tanti Wijaya]], [[Ndapandula Nakashole]] and [[Tom M. Mitchell]].
  
 
== Overview ==
 
== Overview ==
 
Learning to determine when the timevarying facts of a Knowledge Base (KB) have to be updated is a challenging task. Authors propose to learn state changing verbs from [[Wikipedia]] edit history. When a state-changing event, such as a marriage or death, happens to an entity, the infobox on the entity’s Wikipedia page usually gets updated. At the same time, the article text may be updated with verbs either being added or deleted to reflect the changes made to the infobox. Authors use Wikipedia edit history to distantly supervise a method for automatically learning verbs and state changes. Additionally, method uses constraints to effectively map verbs to infobox changes. Authors observe in experiments that when state-changing verbs are added or deleted from an entity’s Wikipedia page text, authors can predict the entity’s infobox updates with 88% precision and 76% recall. One compelling application of verbs is to incorporate them as triggers in methods for updating existing KBs, which are currently mostly static.
 
Learning to determine when the timevarying facts of a Knowledge Base (KB) have to be updated is a challenging task. Authors propose to learn state changing verbs from [[Wikipedia]] edit history. When a state-changing event, such as a marriage or death, happens to an entity, the infobox on the entity’s Wikipedia page usually gets updated. At the same time, the article text may be updated with verbs either being added or deleted to reflect the changes made to the infobox. Authors use Wikipedia edit history to distantly supervise a method for automatically learning verbs and state changes. Additionally, method uses constraints to effectively map verbs to infobox changes. Authors observe in experiments that when state-changing verbs are added or deleted from an entity’s Wikipedia page text, authors can predict the entity’s infobox updates with 88% precision and 76% recall. One compelling application of verbs is to incorporate them as triggers in methods for updating existing KBs, which are currently mostly static.

Revision as of 07:44, 31 July 2019


A Spousal Relation Begins with a Deletion of Engage and Ends with an Addition of Divorce: Learning State Changing Verbs from Wikipedia Revision History
Authors
Derry Tanti Wijaya
Ndapandula Nakashole
Tom M. Mitchell
Publication date
2015
DOI
10.18653/v1/D15-1059
Links
Original

A Spousal Relation Begins with a Deletion of Engage and Ends with an Addition of Divorce: Learning State Changing Verbs from Wikipedia Revision History - scientific work related to Wikipedia quality published in 2015, written by Derry Tanti Wijaya, Ndapandula Nakashole and Tom M. Mitchell.

Overview

Learning to determine when the timevarying facts of a Knowledge Base (KB) have to be updated is a challenging task. Authors propose to learn state changing verbs from Wikipedia edit history. When a state-changing event, such as a marriage or death, happens to an entity, the infobox on the entity’s Wikipedia page usually gets updated. At the same time, the article text may be updated with verbs either being added or deleted to reflect the changes made to the infobox. Authors use Wikipedia edit history to distantly supervise a method for automatically learning verbs and state changes. Additionally, method uses constraints to effectively map verbs to infobox changes. Authors observe in experiments that when state-changing verbs are added or deleted from an entity’s Wikipedia page text, authors can predict the entity’s infobox updates with 88% precision and 76% recall. One compelling application of verbs is to incorporate them as triggers in methods for updating existing KBs, which are currently mostly static.