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 per...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.展开更多
ART Ⅱ网络以模式的相似性量度值为基础,能够对动态的输入模式样本进行自适应的聚类和识别,然而标准的ART Ⅱ网络在输入数据处理过程中,忽略了样本数据中的负数信息和幅值信息,造成信号畸变和"同相位不可分"问题,在权值调整...ART Ⅱ网络以模式的相似性量度值为基础,能够对动态的输入模式样本进行自适应的聚类和识别,然而标准的ART Ⅱ网络在输入数据处理过程中,忽略了样本数据中的负数信息和幅值信息,造成信号畸变和"同相位不可分"问题,在权值调整过程中,聚类中心发生移动,容易造成"模式漂移"现象。针对上述问题结合相关文献提出了引入非线性函数对输入数据进行变换的方法解决"同相位不可分"问题,用待测数据与同一模式类中有限数据的欧氏距离与限定值进行比较实现聚类判定,抑制"模式漂移"现象。用Matlab仿真表明,改进算法性能优于标准算法。展开更多
文摘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.
