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Selecting hierarchical clustering cut points for web person-name disambiguation

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SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 778 - 779

https://doi.org/10.1145/1571941.1572124

Published: 19 July 2009 Publication History

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Abstract

Hierarchical clustering is often used to cluster person-names referring to the same entities. Since the correct number of clusters for a given person-name is not known a priori, some way of deciding where to cut the resulting dendrogram to balance risks of over- or under-clustering is needed. This paper reports on experiments in which outcome-specific and result-set measures are used to learn a global similarity threshold. Results on the Web People Search (WePS)-2 task indicate that approximately 85% of the optimal F1 measure can be achieved on held-out data.

References

[1]

J. Artiles, J. Gonzalo, and S. Sekine. The SemEval-2007 WePS Evaluation. Proceedings of SemEval, 2007.

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[2]

C. S. Manning and H. Schutze. Foundations of Statistical Natural Language Processing, The MIT Press, 500--512.

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[3]

J. Gong and D. Oard. Determine the Entity Number in Hierarchical Clustering for Web Personal Name Disambiguation. Workshop (WePS 2009), 18th WWW Conference, April 2009.

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Cited By

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Emami H(2019)A Graph-based Approach to Person Name Disambiguation in WebACM Transactions on Management Information Systems10.1145/331494910:2(1-25)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1145/3314949
Lee SRo Y(2018)Partial Matching of Facial Expression Sequence Using Over-Complete Transition Dictionary for Emotion RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2015.24963207:4(389-408)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/TAFFC.2015.2496320
Qian YZheng QSakai TYe JLiu J(2015)Dynamic author name disambiguation for growing digital librariesInformation Retrieval Journal10.1007/s10791-015-9261-318:5(379-412)Online publication date: 21-Jul-2015
https://doi.org/10.1007/s10791-015-9261-3
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Index Terms

Selecting hierarchical clustering cut points for web person-name disambiguation
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2009

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Emami H(2019)A Graph-based Approach to Person Name Disambiguation in WebACM Transactions on Management Information Systems10.1145/331494910:2(1-25)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1145/3314949
Lee SRo Y(2018)Partial Matching of Facial Expression Sequence Using Over-Complete Transition Dictionary for Emotion RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2015.24963207:4(389-408)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/TAFFC.2015.2496320
Qian YZheng QSakai TYe JLiu J(2015)Dynamic author name disambiguation for growing digital librariesInformation Retrieval Journal10.1007/s10791-015-9261-318:5(379-412)Online publication date: 21-Jul-2015
https://doi.org/10.1007/s10791-015-9261-3
Xu JLu QLi MLi W(2015)Web Person Disambiguation Using Hierarchical Co-reference ModelComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-18111-0_22(279-291)Online publication date: 2015
https://doi.org/10.1007/978-3-319-18111-0_22
Li XOuyang JZhou XFu B(2014)Adaptive Centroid-Based Clustering Algorithm for Text Document Data2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming10.1109/PAAP.2014.13(63-68)Online publication date: Jul-2014
https://doi.org/10.1109/PAAP.2014.13
Lee SRo Y(2014)Local age group modeling in unconstrained face images for facial age classification2014 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2014.7025279(1395-1399)Online publication date: Oct-2014
https://doi.org/10.1109/ICIP.2014.7025279
Wang FYang YMa ZLi L(2013)A Three-Stage Clustering Framework Based on Multiple Feature Combination for Chinese Person Name DisambiguationProceedings of the 2013 International Conference on Information Science and Cloud Computing Companion10.1109/ISCC-C.2013.33(103-109)Online publication date: 7-Dec-2013
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Zhang DWang JSi LMa WNie JBaeza-Yates RChua TCroft W(2011)Document clustering with universumProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010033(873-882)Online publication date: 24-Jul-2011
https://dl.acm.org/doi/10.1145/2009916.2010033
Chen YHuang C(2010)Exploring personal name disambiguation from name understanding2010 4th International Universal Communication Symposium10.1109/IUCS.2010.5666185(345-349)Online publication date: Oct-2010
https://doi.org/10.1109/IUCS.2010.5666185

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A comparison of retrieval-based hierarchical clustering approaches to person name disambiguation

Online Person Name Disambiguation with Constraints

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