Semi-supervised Approach Based on Co-occurrence Coefficient for Named Entity Recognition on Twitter
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tran, Van Cuong | - |
dc.contributor.author | Hwang, Dosam | - |
dc.contributor.author | Jung, Jason J. | - |
dc.date.accessioned | 2021-08-17T05:40:45Z | - |
dc.date.available | 2021-08-17T05:40:45Z | - |
dc.date.issued | 2015-10 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48531 | - |
dc.description.abstract | The nature characteristics of data in Social Network Services (SNS) are usually short, contain insufficient information, and often are influenced by noise data, thus popular Named Entity Recognition (NER) methods applied for these data could provide wrong results even if they perform well on well-format documents. Most of NER methods are based on supervised learning techniques which often require a large amount of training dataset to train a good classifier. The Conditional Random Fields (CRF) is an example of supervised learning method, which is a statistical modeling method to predict labels for sequences of input samples. Weak point of these method is only perform well on well-format sentences. However the proper sentences are not used frequently in SNS, such as a lot of tweets on Twitter are combinations of independent terms which are implicitly belonged to a context of a certain discussion topic. In this paper, we propose a method to extract named entities from Social Data using a semi-supervised learning method, it is an extension of CRF method which adapts the new challenge with segmentations of data depending on its context rather considering entire dataset. In experiments, The method is applied on a dataset collected from Twitter, which includes 8,624 tweets for training with 1,915 labeled tweets and 1,690 tweets for testing. Our system product a promised result with the F score of the classification result be approximated to 83.9%. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Semi-supervised Approach Based on Co-occurrence Coefficient for Named Entity Recognition on Twitter | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/NICS.2015.7302179 | - |
dc.identifier.bibliographicCitation | PROCEEDINGS OF 2015 2ND NATIONAL FOUNDATION FOR SCIENCE AND TECHNOLOGY DEVELOPMENT CONFERENCE ON INFORMATION AND COMPUTER SCIENCE NICS 2015, pp 141 - 146 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000380487700024 | - |
dc.identifier.scopusid | 2-s2.0-84959862358 | - |
dc.citation.endPage | 146 | - |
dc.citation.startPage | 141 | - |
dc.citation.title | PROCEEDINGS OF 2015 2ND NATIONAL FOUNDATION FOR SCIENCE AND TECHNOLOGY DEVELOPMENT CONFERENCE ON INFORMATION AND COMPUTER SCIENCE NICS 2015 | - |
dc.type.docType | Proceedings Paper | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.