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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.

has been cited by the following article:

• TITLE: Adaptive Resonance Theory Based Two-Stage Chinese Name Disambiguation

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

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

  JOURNAL NAME: International Journal of Intelligence Science, Vol.2 No.4, October 30, 2012

  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.