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Semi-supervised Domain Specified Event Extraction from Social Media
http://hdl.handle.net/20.500.12678/0000005354
http://hdl.handle.net/20.500.12678/0000005354f87f0ec3-f517-495f-9fc0-dedc97df77d1
46eaa463-d6a1-429e-b180-5ab0e5a6dc78
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Semi-supervised Domain Specified Event Extraction from Social Media.pdf (126 Kb)
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Semi-supervised Domain Specified Event Extraction from Social Media | |||||
Language | en | |||||
Publication date | 2020-09-14 | |||||
Authors | ||||||
San San Nwe | ||||||
Nang Saing Moon Kham | ||||||
Description | ||||||
Social media has quickly become popular as an important means that people, organizations use to spread information of divert events for various purposes, ranging from business intelligence to nation security. However, the language used in Twitter is heavily informal, ungrammatical, short and dynamic. Automatically detecting and categorizing events using streamed data is a difficult task, due to the presence of noise and irrelevant information. Therefore, as an emerging research area, event analysis from social media, Twitter has attracted much attention since 2010 and there are many attempts to detect and categorize events from social media. This paper proposes a framework to identify the events from twitter in a semisupervised manner for targeted domain in specific location with SVM in combination with the corpus. The demonstration shown that, with the selective use of a variety of unlabeled data, the SVM models outperform a strong state-ofthe- art supervised classification model |
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Keywords | ||||||
Social Media, Twitter, Semisupervised, Events, SVM | ||||||
Conference papers | ||||||
ICCA-2018 | ||||||
22-23 February, 2018 | ||||||
International Conference on Computer Applications | ||||||
Yangon, Myanmar |