Map to humans and reduce error - Crowdsourcing for deduplication applied to digital libraries

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 1970-1974

  Georgescu, Mihai ^a   Pham, Dang Duc ^a   Firan, Claudiu S ^a   Nejdl, Wolfgang ^a   Gaugaz, Julien ^a  

^a L3S RESEARCH CENTER   (Germany)

Author keywords

active learning; crowdsourcing; duplicate detection; machine learning; optimization

Indexed keywords

ACTIVE LEARNING; CROWDSOURCING; DEDUPLICATION; DUPLICATE DETECTION; FEEDBACK MECHANISMS; HUMAN ASSESSORS; ONLINE PUBLICATIONS; ONLINE USERS; REAL TIME; TRAINING COSTS; TRAINING DATA;

KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; METADATA; OPTIMIZATION;

DIGITAL LIBRARIES;

EID: 84871066615     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2398554     Document Type: Conference Paper

References (19)

1. J. Attenberg and F. Provost. Why Label when you can Search? Alternatives to Active Learning for Applying Human Resources to Build Classification Models Under Extreme Class Imbalance. KDD '10. ACM, 2010.
2. O. Benjelloun, H. Garcia-Molina, D. Menestrina, Q. Su, S. E. Whang, and J. Widom. Swoosh: A Generic Approach to Entity Resolution. The VLDB Journal, 18(1), 2009.
3. D. Cohn, L. Atlas, and R. Ladner. Improving Generalization with Active Learning. Mach. Learn., 15(2), 1994.
4. A. P. Dawid and A. M. Skene. Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm. Applied Statistics, 28(1), 1979.
5. G. Demartini, D. E. Difallah, and P. Cudré-Mauroux. ZenCrowd: Leveraging Probabilistic Reasoning and Crowdsourcing Techniques for Large-Scale Entity Linking. WWW '12. ACM, 2012.
6. A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate Record Detection: A Survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 2007.
7. C. S. Firan, M. Georgescu, W. Nejdl, and X. Sun. FreeSearch - Literature Search in a Natural Way. In HCIR '11, 2011.
8. A. Y. Halevy, X. Dong, J. Madhavan, A. Y. Halevy, and J. Madhavan. Reference Reconciliation in Complex Information Spaces. In SIGMOD '05. ACM Press, 2005.
9. M. A. Hernández and S. J. Stolfo. Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem. Data Min. Knowl. Discov., 2(1), 1998.
10. E. Ioannou, C. Niederée, and W. Nejdl. Probabilistic Entity Linkage for Heterogeneous Information Spaces. In CAiSE, 2008.
11. P. G. Ipeirotis, F. Provost, and J. Wang. Quality management on amazon mechanical turk. HCOMP '10. ACM, 2010.
12. M. Lukasiewycz, M. Glaß, F. Reimann, and J. Teich. Opt4J - A Modular Framework for Meta-heuristic Optimization. In GECCO '11, 2011.
13. Z. Miklós, N. Bonvin, P. Bouquet, M. Catasta, D. Cordioli, P. Fankhauser, J. Gaugaz, E. Ioannou, H. Koshutanski, A. Maña, C. Niederée, T. Palpanas, and H. Stoermer. From Web Data to Entities and Back. CAiSE, 2010.
14. V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. Learning from Crowds. Journal of Machine Learning Research, 11, 2010.
15. S. Sarawagi and A. Bhamidipaty. Interactive Deduplication Using Active Learning. KDD '02. ACM, 2002.
16. V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers. KDD '08. ACM, 2008.
17. O. Tamuz, C. Liu, S. Belongie, O. Shamir, and A. Kalai. Adaptively Learning the Crowd Kernel. In ICML, 2011.
18. L. von Ahn. Human Computation. In CIVR, 2009.
19. M.-C. Yuen, L.-J. Chen, I. King, and I. King. A Survey of Human Computation Systems. In CSE (4), 2009.