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Learning an accurate entity resolution model from crowdsourced labels

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65191

Title: Learning an accurate entity resolution model from crowdsourced labels
Authors: Wang, Jingjing Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Kashima, Hisashi Browse this author
Keywords: Entity resolution
Crowdsourcing
Link prediction
Dimensionality reduction
Issue Date: 9-Jan-2014
Publisher: ACM
Citation: ICUIMC 2014 : January 9-11, Siem Reap, Cambodia ; proceedings, ISBN: 978-1-4503-2644-5
Start Page: 1
End Page: 8
Publisher DOI: 10.1145/2557977.2558060
Abstract: We investigated the use of supervised learning methods that use labels from crowd workers to resolve entities. Although obtaining labeled data by crowdsourcing can reduce time and cost, it also brings challenges (e.g., coping with the variable quality of crowdgenerated data). First, we evaluated the quality of crowd-generated labels for actual entity resolution data sets. Then, we evaluated the prediction accuracy of two machine learning methods that use labels from crowd workers: a conventional LPP method using consensus labels obtained by majority voting and our proposed method that combines multiple Laplacians directly by using crowdsourced data. We discussed the relationship between the accuracy of workers’ labels and the prediction accuracy of the two methods.
Conference Name: International Conference on Ubiquitous Information Management and Communication (ICUIMC)
Conference Sequence: 8
Conference Place: Siem Reap
Rights: ©2014 ACM. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, ICUIMC '14 Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, http://doi.acm.org/10.1145/2557977.2558060
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/65191
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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