Semi-supervised Domain Specified Event Extraction from Social Media

San San Nwe; Nang Saing Moon Kham

MERAL Myanmar Education Research and Learning Portal

Index Tree

RootNode
- Co-operative College, Mandalay
- Cooperative College, Phaunggyi
- Co-operative University, Sagaing
- Co-operative University, Thanlyin
- Dagon University
- Kyaukse University
- Laquarware Technological college
- Mandalay Technological University
- Mandalay University of Distance Education
- Mandalay University of Foreign Languages
- Maubin University
- Mawlamyine University
- Meiktila University
- Mohnyin University
- Myanmar Institute of Information Technology
- Myanmar Maritime University
- National Management Degree College
- Naypyitaw State Academy
- Pathein University
- Sagaing University
- Sagaing University of Education
- Taunggyi University
- Technological University, Hmawbi
- Technological University (Kyaukse)
- Technological University Mandalay
- University of Computer Studies, Mandalay
- University of Computer Studies Maubin
- University of Computer Studies, Meikhtila
- University of Computer Studies Pathein
- University of Computer Studies, Taungoo
- University of Computer Studies, Yangon
- University of Dental Medicine Mandalay
- University of Dental Medicine, Yangon
- University of Information Technology
- University of Mandalay
- University of Medicine 1
- University of Medicine 2
- University of Medicine Mandalay
- University of Myitkyina
- University of Public Health, Yangon
- University of Veterinary Science
- University of Yangon
- West Yangon University
- Yadanabon University
- Yangon Technological University
- Yangon University of Distance Education
- Yangon University of Economics
- Yangon University of Education
- Yangon University of Foreign Languages
- Yezin Agricultural University
- New Index

Item

{"_buckets": {"deposit": "46eaa463-d6a1-429e-b180-5ab0e5a6dc78"}, "_deposit": {"created_by": 45, "id": "5354", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5354"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5354", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Semi-supervised Domain Specified Event Extraction from Social Media", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Social media has quickly become\npopular as an important means that people,\norganizations use to spread information of divert\nevents for various purposes, ranging from\nbusiness intelligence to nation security.\nHowever, the language used in Twitter is heavily\ninformal, ungrammatical, short and dynamic.\nAutomatically detecting and categorizing events\nusing streamed data is a difficult task, due to the\npresence of noise and irrelevant information.\nTherefore, as an emerging research area, event\nanalysis from social media, Twitter has attracted\nmuch attention since 2010 and there are many\nattempts to detect and categorize events from\nsocial media. This paper proposes a framework\nto identify the events from twitter in a semisupervised\nmanner for targeted domain in\nspecific location with SVM in combination with\nthe corpus. The demonstration shown that, with\nthe selective use of a variety of unlabeled data,\nthe SVM models outperform a strong state-ofthe-\nart supervised classification\nmodel"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Social Media"}, {"interim": "Twitter"}, {"interim": "Semisupervised"}, {"interim": "Events"}, {"interim": "SVM"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-09-14"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Semi-supervised Domain Specified Event Extraction from Social Media.pdf", "filesize": [{"value": "126 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 126000.0, "url": {"url": "https://meral.edu.mm/record/5354/files/Semi-supervised Domain Specified Event Extraction from Social Media.pdf"}, "version_id": "3d936596-12f8-45d2-9945-0069b9513cc0"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICCA-2018", "subitem_c_date": "22-23 February, 2018", "subitem_conference_title": "International Conference on Computer Applications", "subitem_place": "Yangon, Myanmar"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "San San Nwe"}, {"subitem_authors_fullname": "Nang Saing Moon Kham"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2020-09-14"}, "item_title": "Semi-supervised Domain Specified Event Extraction from Social Media", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005354", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-14"}, "publish_date": "2020-09-14", "publish_status": "0", "recid": "5354", "relation": {}, "relation_version_is_last": true, "title": ["Semi-supervised Domain Specified Event Extraction from Social Media"], "weko_shared_id": -1}

Semi-supervised Domain Specified Event Extraction from Social Media

http://hdl.handle.net/20.500.12678/0000005354

Preview

Name / File	License	Actions
Semi-supervised Domain Specified Event Extraction from Social Media.pdf (126 Kb)

Publication type
		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
Keywords
		Social Media, Twitter, Semisupervised, Events, SVM
Conference papers
		ICCA-2018
		22-23 February, 2018
		International Conference on Computer Applications
		Yangon, Myanmar

Back

views

downloads

See details

	Views	Downloads

Versions

Ver.1

2020-09-14 08:12:00.143246

Show All versions

Export

OAI-PMH

DublinCore

Other Formats

JSON

Index Link

Index Tree

Item

Semi-supervised Domain Specified Event Extraction from Social Media

Versions

Share

Export