论文标题
贝叶斯学习，贪婪的聚集聚类方法和作者名称歧义问题的评估技术
A Bayesian Learning, Greedy agglomerative clustering approach and evaluation techniques for Author Name Disambiguation Problem
论文作者
论文摘要
作者的名字通常是由于同一作者以不同的名字出现的，并且具有相似名称的多个作者。它在将学术工作与写作的人相关联时造成了困难，从而引入了信用归因，书目分析，数字图书馆中的搜索者和专家发现的不准确性。文献中提出了多种歧义作者名称的技术。我试图专注于针对歧义作者姓名的研究工作。我首先遵循常规方法，然后讨论评估技术和聚类模型，最终导致贝叶斯学习和贪婪的团聚方法。我认为，这项集中的评论对研究界将很有用，因为它讨论了应用于全球范围内积极使用的非常大的真实数据库的技术。使用的贝叶斯和贪婪的团聚方法将有助于更好地解决和问题。最后，我尝试概述以后的一些指示
Author names often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library, and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. I try to focus on the research efforts targeted to disambiguate author names. I first go through the conventional methods, then I discuss evaluation techniques and the clustering model which finally leads to the Bayesian learning and Greedy agglomerative approach. I believe this concentrated review will be useful for the research community because it discusses techniques applied to a very large real database that is actively used worldwide. The Bayesian and the greedy agglomerative approach used will help to tackle AND problems in a better way. Finally, I try to outline a few directions for future work
