Adaptive Resonance Theory Based Two-Stage Chinese Name Disambiguation

Xin Wang, Yuanchao Liu, Xiaolong Wang, Ming Liu, Bingquan Liu

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

DOI: 10.4236/ijis.2012.24011                

Abstract

It’s common that different individuals share the same name, which makes it time-consuming to search information of a particular individual on the web. Name disambiguation study is necessary to help users find the person of interest more readily. In this paper, we propose an Adaptive Resonance Theory (ART) based two-stage strategy for this problem. We get a first-stage clustering result with ART1 model and then merge similar clusters in the second stage. Our strategy is a mimic process of manual disambiguation and need not to predict the number of clusters, which makes it competent for the disambiguation task. Experimental results show that, in comparison with the agglomerative clustering method, our strategy improves the performance by respectively 0.92% and 5.00% on two kinds of name recognition results.

Keywords

Name Disambiguation; Adaptive Resonance Theory; Text Clustering; Natural Language Processing

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Artiles, S. Sekine and J. Gonzalo, “Web People Search: Results of the First Evaluation and the Plan for the Second,” Proceeding of the 17th International Conference on World Wide Web, Beijing, 21-25 April 2008, pp. 1071-1072.
[2]	A. Bagga and B. Baldwin, “Entity-Based Cross-Document Coreferencing Using the Vector Space Model,” Proceedings of the 17th International Conference on Computational Linguistics, Montreal, 10-14 August 1998, pp. 79-85.
[3]	G. S. Mann and D. Yarowsky, “Unsupervised Personal Name Disambiguation,” Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003, Edmonton, 31 May 2003, pp. 33-40.
[4]	J. Hassell, B. Aleman-Meza and I. Arpinar, “Ontology-Driven Automatic Entity Disambiguation in Unstructured Text,” Proceedings of the 5th International Semantic Web Conference, Springer Berlin/Heidelberg, 2006, pp. 44-57.
[5]	D. Bollegala, Y. Matsuo and M. Ishizuka, “Disambiguating Personal Names on the Web Using Automatically Extracted Key Phrases,” European Conference on Artificial Intelligence, 2006, pp. 553-557.
[6]	R. C. Bunescu and M. Pasca, “Using Encyclopedic Knowledge for Named entity Disambiguation,” Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, The Association for Computer Linguistics, 2006, pp. 9-16.
[7]	X. Han and L. Sun, “A Generative Entity-Mention Model for Linking Entities with Knowledge Base,” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, 2011, pp. 945-954.
[8]	Q. M. Vu, A. Takasu and J. Adachi, “Improving the Performance of Personal Name Disambiguation Using Web Directories,” Information Processing & Management, Vol. 44, No. 4, 2008, pp. 1546-1561.
[9]	X. Han and J. Zhao, “CASIANED: Web Personal Name Disambiguation Based on Professional Categorization,” 2nd Web People Search Evaluation Workshop (WePS 2009) at 18th WWW Conference, Madrid, 2009.
[10]	X. Zhou, C. Li, M. Hu and H. Wang, “Chinese Name Disambiguation Based on Exclusive Character Attributes,” Proceedings of the 6th China National Conference on Information Retrieval (CCIR2010), Harbin, 2010, pp. 333-340.
[11]	T. Pedersen, A. Purandare and A. Kulkarni, “Name Discrimination by Clustering Similar Contexts,” Computational Linguistics and Intelligent Text Processing, Vol. 3406, 2005, pp. 226-237.
[12]	E. Lefever, T. Fayruzov, V. Hoste and M. De Cock, “Clustering Web People Search Results Using Fuzzy Ants,” Information Sciences, Vol. 180, No. 17, 2010, pp. 3192-3209.
[13]	M. Ikeda, S. Ono, I. Sato, M. Yoshida and H. Nakagawa, “Person Name Disambiguation on the Web by Two-Stage Clustering,” 2nd Web People Search Evaluation Workshop (WePS 2009) at 18th WWW Conference, Madrid, 2009.
[14]	H. Ding, T. Xiao and J. Zhu, “A Mutil-Stage Clustering Approach to Chinese Person Name Disambiguation,” Proceedings of the 6th China National Conference on Information Retrieval (CCIR2010), Harbin, 2010, pp. 316-324.
[15]	R. Bekkerman and A. McCallum, “Disambiguating Web Appearances of People in a Social Network,” Proceedings of the 14th international conference on World Wide Web, Beijing, 2005, pp. 463-470.
[16]	B. Malin, “Unsupervised Name Disambiguation via Social Network Similarity,” SIAM International Conference on Data Mining, Newport Beach, 2005, pp. 93-102.
[17]	J. Lang, et al., “Person Name Disambiguation of Searching Results Using Social Network,” Chinese Journal of Computers, No.7, 2009, pp. 1365-1374.
[18]	S. Grossberg, “Adaptive Pattern Classification and Universal Recoding: I. Parallel Development and Coding of Neural Feature Detectors,” Formal Aspects of Computing, Vol. 23, No. 3, 1976, pp. 121-134.
[19]	S. Grossberg, “Adaptive Pattern Classification and Universal Recoding: II. Feedback, Expectation, Olfaction, Illusions,” Formal Aspects of Computing, Vol. 23, No. 4, 1976, pp. 187-202
[20]	G. A. Carpenter and S. Grossberg, “ART 2: Stable Self-Organization of Pattern Recognition Codes for Analog Input Patterns,” Applied Optics, Vol. 26, No. 23, 1987, pp. 4919-4930.
[21]	G. A. Carpenter, S. Grossberg and D. B. Rosen, “ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition,” Neural Networks, Vol. 4, No. 4, 1991, pp. 493-504.
[22]	G. A. Carpenter and S. Grossberg, “ART 3: Hierarchical Search Using Chemical Transmitters in Self-Organizing Pattern Recognition Architectures,” Neural Networks, Vol. 3, No. 2, 1990, pp. 129-152.
[23]	G. A. Carpenter, S. Grossberg and J. H. Reynolds, “ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network,” Neural Networks, Vol. 4, No. 5, 1991, pp. 565-588.
[24]	G. A. Carpenter, S. Grossberg and D. B. Rosen, “Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System,” Neural Networks, Vol. 4, No. 6, 1991, pp. 759-771.
[25]	P. J. G. Lisboa, “A Review of Evidence of Health Benefit from Artificial Neural Networks in Medical Intervention,” Neural Networks, Vol. 15, No. 1, 2002, pp. 11-39.
[26]	F. Chen and S. Liu, “A Neural-Network Approach to Recognize Defect Spatial Pattern In Semiconductor Fabrication,” IEEE Transactions on Semiconductor Manufacturing, Vol. 13, No. 3, 2000, pp. 366-373.
[27]	D. Dasgupta and H. Brian, “Mobile Security Agents for Network Traffic Analysis,” Proceedings of DARPA Information Survivability Conference and Exposition, Vol. 2, 2001, pp. 332-340.
[28]	J. Artiles, J. Gonzalo and S. Sekine, “The SemEval-2007 WePS Evaluation: Establishing a Benchmark for the Web People Search Task,” Proceedings of the 4th International Workshop on Semantic Evaluations, 2007, pp. 64-69.
[29]	A. Bagga and B. Baldwin, “Algorithms for Scoring Co-Reference Chains,” The Linguistic Co-Reference Workshop at The First International Conference on Language Resources and Evaluation (LREC’98), 1998, pp. 563-566.