NDLI: Map to humans and reduce error: crowdsourcing for deduplication applied to digital libraries

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Map to humans and reduce error: crowdsourcing for deduplication applied to digital libraries

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Map to humans and reduce error: crowdsourcing for deduplication applied to digital libraries

Content Provider	ACM Digital Library
Author	Nejdl, Wolfgang Georgescu, Mihai Firan, Claudiu S. Pham, Dang Duc Gaugaz, Julien
Abstract	Detecting duplicate entities, usually by examining metadata, has been the focus of much recent work. Several methods try to identify duplicate entities, while focusing either on accuracy or on efficiency and speed - with still no perfect solution. We propose a combined layered approach for duplicate detection with the main advantage of using Crowdsourcing as a training and feedback mechanism. By using Active Learning techniques on human provided examples, we fine tune our algorithm toward better duplicate detection accuracy. We keep the training cost low by gathering training data on demand for borderline cases or for inconclusive assessments. We apply our simple and powerful methods to an online publication search system: First, we perform a coarse duplicate detection relying on publication signatures in real time. Then, a second automatic step compares duplicate candidates and increases accuracy while adjusting based on both feedback from our online users and from Crowdsourcing platforms. Our approach shows an improvement of 14% over the untrained setting and is at only 4% difference to the human assessors in accuracy.
Starting Page	1970
Ending Page	1974
Page Count	5
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398554
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Duplicate detection Active learning Machine learning Optimization
Content Type	Text
Resource Type	Article

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