| 一种论文作者重名消歧方法 |
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| 引用本文： | 仇国华,赵 华.一种论文作者重名消歧方法[J].教育技术导刊,2020,19(3):111-115. |
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| 作者姓名： | 仇国华 赵 华 |
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| 作者单位： | 山东科技大学 计算机科学与工程学院，山东 青岛 266590 |
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| 基金项目： | 教育部人文社会科学研究青年基金项目（16YJCZH154） |
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| 摘 要： | 作者重名消歧是一个重要又复杂的研究课题，在科技文献检索工作中，作者重名问题势必会降低文献检索的效率和准确性，影响工作进度。提出一种改进粒子群算法优化的BP（Back Propagation）神经网络算法，以解决作者重名消歧问题。首先引入Beta分布的动态惯性权重，提高算法全局搜索能力|其次利用改进粒子群算法优化的权值和阈值，作为BP神经网络的初始权值和阈值进行模型训练，以加快模型训练速度|最后通过特征评价函数过滤式选取排序较优的M维特征子集作为输入层特征向量训练模型，得到最终预测结果，从而精确区分重名的作者。实验研究表明，该模型对重名作者身份的预测准确率可达89.01%，证明了该算法的有效性。
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| 关 键 词： | 重名消歧 PSO算法 BP神经网络 动态惯性权重 特征评价函数 |
| 收稿时间： | 2019-11-21 |
A Method of Distinguishing Distinguished Names of Authors |
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| QIU Guo-hua,ZHAO Hua.A Method of Distinguishing Distinguished Names of Authors[J].Introduction of Educational Technology,2020,19(3):111-115. |
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| Authors: | QIU Guo-hua ZHAO Hua |
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| Institution: | College of Computer Science and Engineering， Shandong University of Science and Technology，Qingdao 266590，China |
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| Abstract: | The author’s name and disambiguation is an important and complicated research topic. In the retrieval of scientific literature， the author’s name problem will inevitably reduce the efficiency and accuracy of literature retrieval and affect the progress of the work. In this paper， a back propagation（BP） neural network algorithm with improved particle swarm optimization is proposed to solve the problem of author’s name disambiguation. Firstly， the dynamic inertia weight of Beta distribution is introduced to improve the global search ability of the algorithm. Secondly， the weight and threshold of the improved particle swarm optimization algorithm are used as the initial weight and threshold of BP neural network to train the model to speed up the training of the model. The feature evaluation function is used to filter and select the M-dimensional feature subsets with better ranking as the input layer feature vector training model to obtain the final prediction result， so as to accurately distinguish the authors of the duplicate names. The experimental results show that the prediction accuracy of the model can be improved to 89.01%， which proves the effectiveness of the algorithm. |
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| Keywords: | duplicate disambiguation PSO algorithm BP neural network dynamic inertia weight feature evaluation function |
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