Self-improving instructional plans on the level of student categories

With the popularity of the Internet and social media platforms, information that is potentially useful in disaster response becomes available online in the hours and days immediately following a disaster. The use of information extraction in retrieving relevant disaster information from all these cr...

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Main Authors: Regalado, Ralph Vincent J., Chua, Jenina L., Co, Justin L.
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Published: Animo Repository 2014
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/4443
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-52892022-01-05T00:56:50Z Self-improving instructional plans on the level of student categories Regalado, Ralph Vincent J. Chua, Jenina L. Co, Justin L. With the popularity of the Internet and social media platforms, information that is potentially useful in disaster response becomes available online in the hours and days immediately following a disaster. The use of information extraction in retrieving relevant disaster information from all these crowdsourced data would provide more information coming from both official reports, and the affected people themselves which in turn facilitate better decision making environments for disaster managers. This paper describes a system which performs an adaptive information retrieval of disaster related information coming from Twitter. Result shows 94.33% accuracy when extracting disaster and location information in the typhoon corpus while 90.79% accuracy for the fire corpus. © 2014 IEEE. 2014-03-23T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/4443 info:doi/10.1109/ICACSIS.2014.7065859 Faculty Research Work Animo Repository Intelligent tutoring systems Cognition Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Intelligent tutoring systems
Cognition
Computer Sciences
spellingShingle Intelligent tutoring systems
Cognition
Computer Sciences
Regalado, Ralph Vincent J.
Chua, Jenina L.
Co, Justin L.
Self-improving instructional plans on the level of student categories
description With the popularity of the Internet and social media platforms, information that is potentially useful in disaster response becomes available online in the hours and days immediately following a disaster. The use of information extraction in retrieving relevant disaster information from all these crowdsourced data would provide more information coming from both official reports, and the affected people themselves which in turn facilitate better decision making environments for disaster managers. This paper describes a system which performs an adaptive information retrieval of disaster related information coming from Twitter. Result shows 94.33% accuracy when extracting disaster and location information in the typhoon corpus while 90.79% accuracy for the fire corpus. © 2014 IEEE.
format text
author Regalado, Ralph Vincent J.
Chua, Jenina L.
Co, Justin L.
author_facet Regalado, Ralph Vincent J.
Chua, Jenina L.
Co, Justin L.
author_sort Regalado, Ralph Vincent J.
title Self-improving instructional plans on the level of student categories
title_short Self-improving instructional plans on the level of student categories
title_full Self-improving instructional plans on the level of student categories
title_fullStr Self-improving instructional plans on the level of student categories
title_full_unstemmed Self-improving instructional plans on the level of student categories
title_sort self-improving instructional plans on the level of student categories
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/faculty_research/4443
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