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Bootstrapping active name disambiguation with crowdsourcing

Authors:

Alok ChoudharyAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1213 - 1216

https://doi.org/10.1145/2505515.2507858

Published: 27 October 2013 Publication History

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Abstract

Name disambiguation is a challenging and important problem in many domains, such as digital libraries, social media management and people search systems. Traditional methods, based on direct assignment using supervised machine learning techniques, seem to be the most effective, but their performances are highly dependent on the amount of training data, while large data annotation can be expensive and time-consuming requiring hours of manual inspection by a domain expert. To efficiently acquire labeled data, we propose a bootstrapping algorithm for the name disambiguation task based on active learning and crowdsourced labeling. We show that the proposed method can leverage the advantages of exploration and exploitation by combining two strategies, thereby improving the overall quality of the training data at minimal expense. The experimental results on two datasets DBLP and ArnetMiner demonstrate the superiority of our framework over existing methods.

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Cited By

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Correia AGuimarães DParedes HFonseca BPaulino DTrigo LBrazdil PSchneider DGrover AJameel S(2023)NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity DetectionIEEE Transactions on Human-Machine Systems10.1109/THMS.2023.331929053:6(1017-1026)Online publication date: Dec-2023
https://doi.org/10.1109/THMS.2023.3319290
Correia AGuimaraes DPaulino DJameel SSchneider DFonseca BParedes H(2021)AuthCrowd: Author Name Disambiguation and Entity Matching using Crowdsourcing2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437769(150-155)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437769
Zhang BDundar MDave VHasan MDownie SZhu QHahn J(2019)Dirichlet process gaussian mixture for active online name disambiguation by particle filterProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00045(269-278)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1109/JCDL.2019.00045
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Index Terms

Bootstrapping active name disambiguation with crowdsourcing
1. Information systems
  1. Information retrieval

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Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Correia AGuimarães DParedes HFonseca BPaulino DTrigo LBrazdil PSchneider DGrover AJameel S(2023)NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity DetectionIEEE Transactions on Human-Machine Systems10.1109/THMS.2023.331929053:6(1017-1026)Online publication date: Dec-2023
https://doi.org/10.1109/THMS.2023.3319290
Correia AGuimaraes DPaulino DJameel SSchneider DFonseca BParedes H(2021)AuthCrowd: Author Name Disambiguation and Entity Matching using Crowdsourcing2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437769(150-155)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437769
Zhang BDundar MDave VHasan MDownie SZhu QHahn J(2019)Dirichlet process gaussian mixture for active online name disambiguation by particle filterProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00045(269-278)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1109/JCDL.2019.00045
Feyisetan OSimperl ELuczak-Roesch MTinati RShadbolt N(2018)An extended study of content and crowdsourcing-related performance factors in named entity annotationSemantic Web10.3233/SW-1702829:3(355-379)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/SW-170282
Qian YZheng QSakai TYe JLiu J(2015)Dynamic author name disambiguation for growing digital librariesInformation Retrieval10.1007/s10791-015-9261-318:5(379-412)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1007/s10791-015-9261-3

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Web personal name disambiguation based on reference entity tables mined from the web

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