NDLI: Semi-supervised approach based on co-occurrence coefficient for named entity recognition on Twitter

Content Provider	IEEE Xplore Digital Library
Author	Van Cuong Tran Dosam Hwang Jung, J.J.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Eng., Chung-Ang Univ., Seoul, South Korea (Jung, J.J.) \|\| Dept. of Comput. Eng., Yeungnam Univ., Gyeongsan, South Korea (Van Cuong Tran; Dosam Hwang)
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%.
Starting Page	141
Ending Page	146
File Size	434512
Page Count	6
File Format	PDF
ISBN	9781467366397
e-ISBN	9781467366403
DOI	10.1109/NICS.2015.7302179
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-16
Publisher Place	Vietnam
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Context Clustering algorithms Tagging Twitter Feature extraction Data mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Bio Named Entity Recognition Based on Co-training Algorithm

Semi-supervised learning for named entity recognition using weakly labeled training data

Named Entity Relation Extraction Based on Multiple Features

Semi-supervised expert metadata extraction based on co-training style

Ensemble based active annotation for biomedical named entity recognition

Named Entity Recognition for Malayalam language: A CRF based approach

Syntax-based semi-supervised named entity tagging.

A Semi-supervised Algorithm for Indonesian Named Entity Recognition

A Semi-supervised Learning Approach to Arabic Named Entity Recognition

Semi-supervised approach based on co-occurrence coefficient for named entity recognition on Twitter

Similar Documents

Bio Named Entity Recognition Based on Co-training Algorithm

Semi-supervised learning for named entity recognition using weakly labeled training data

Named Entity Relation Extraction Based on Multiple Features

Semi-supervised expert metadata extraction based on co-training style

Ensemble based active annotation for biomedical named entity recognition

Named Entity Recognition for Malayalam language: A CRF based approach

Syntax-based semi-supervised named entity tagging.

A Semi-supervised Algorithm for Indonesian Named Entity Recognition

A Semi-supervised Learning Approach to Arabic Named Entity Recognition

Semi-supervised approach based on co-occurrence coefficient for named entity recognition on Twitter