Difference between revisions of "Labeling News Topic Threads with Wikipedia Entries"

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{{Infobox work
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| title = Labeling News Topic Threads with Wikipedia Entries
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| date = 2009
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| authors = [[Tomoki Okuoka]]<br />[[Tomokazu Takahashi]]<br />[[Daisuke Deguchi]]<br />[[Ichiro Ide]]<br />[[Hiroshi Murase]]
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| doi = 10.1109/ISM.2009.67
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| link = http://ieeexplore.ieee.org/document/5363688/
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}}
 
'''Labeling News Topic Threads with Wikipedia Entries''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Tomoki Okuoka]], [[Tomokazu Takahashi]], [[Daisuke Deguchi]], [[Ichiro Ide]] and [[Hiroshi Murase]].
 
'''Labeling News Topic Threads with Wikipedia Entries''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Tomoki Okuoka]], [[Tomokazu Takahashi]], [[Daisuke Deguchi]], [[Ichiro Ide]] and [[Hiroshi Murase]].
  
 
== Overview ==
 
== Overview ==
 
Wikipedia is a famous online encyclopedia. However most [[Wikipedia]] entries are mainly explained by text, so it will be very informative to enhance the contents with multimedia information such as videos. Thus authors are working on a method to extend information of Wikipedia entries by means of broadcast videos which explain the entries. In this work, authors focus especially on news videos and Wikipedia entries about news events. In order to extend information of Wikipedia entries, it is necessary to link news videos and Wikipedia entries. So the main issue will be on a method that labels news videos with Wikipedia entries automatically. In this way, explanations could be more detailed with news videos can be exhibited, and the context of the news events should become easier to understand. Through experiments, news videos were accurately labeled with Wikipedia entries with a precision of 86% and a recall of 79%.
 
Wikipedia is a famous online encyclopedia. However most [[Wikipedia]] entries are mainly explained by text, so it will be very informative to enhance the contents with multimedia information such as videos. Thus authors are working on a method to extend information of Wikipedia entries by means of broadcast videos which explain the entries. In this work, authors focus especially on news videos and Wikipedia entries about news events. In order to extend information of Wikipedia entries, it is necessary to link news videos and Wikipedia entries. So the main issue will be on a method that labels news videos with Wikipedia entries automatically. In this way, explanations could be more detailed with news videos can be exhibited, and the context of the news events should become easier to understand. Through experiments, news videos were accurately labeled with Wikipedia entries with a precision of 86% and a recall of 79%.

Revision as of 20:26, 15 June 2019


Labeling News Topic Threads with Wikipedia Entries
Authors
Tomoki Okuoka
Tomokazu Takahashi
Daisuke Deguchi
Ichiro Ide
Hiroshi Murase
Publication date
2009
DOI
10.1109/ISM.2009.67
Links
Original

Labeling News Topic Threads with Wikipedia Entries - scientific work related to Wikipedia quality published in 2009, written by Tomoki Okuoka, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide and Hiroshi Murase.

Overview

Wikipedia is a famous online encyclopedia. However most Wikipedia entries are mainly explained by text, so it will be very informative to enhance the contents with multimedia information such as videos. Thus authors are working on a method to extend information of Wikipedia entries by means of broadcast videos which explain the entries. In this work, authors focus especially on news videos and Wikipedia entries about news events. In order to extend information of Wikipedia entries, it is necessary to link news videos and Wikipedia entries. So the main issue will be on a method that labels news videos with Wikipedia entries automatically. In this way, explanations could be more detailed with news videos can be exhibited, and the context of the news events should become easier to understand. Through experiments, news videos were accurately labeled with Wikipedia entries with a precision of 86% and a recall of 79%.