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A Novel method for Reviewer Assignment Problem based on Reviewers' Profile and Fuzzy Graph Connectivity Measure

机译:基于审稿人档案和模糊图连通性测度的审稿人分配问题新方法

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Reviewer Assignment Problem (RAP) is defined as a problem of assigning the most suitable expert to the proposal. which is a very important task for any research organization In literature many solutions have been proposed for solving RAP but most of them do not deal with the imprecision associated with the problem. In this paper, a novel approach has been proposed for assigning proposals to legitimate experts based on their previous history and domain expertise. For the purpose, we have first extracted the most important words from the author’s submitted paper and reviewer’s published articles by constructing a fuzzy graph and applying fuzzy graph centrality measures on it. We have then created fuzzy sets for the selected keywords and their weights (for both author’s papers and reviewer’s publications). Afterwards, WordNet have been used to find the diatance beween the fuzzy sets represented for author's papers reviewers' papers. Finally, fuzzy extension principle is applied on the sets to slect the top 3 experts for reviewing the proposal. Experimentation shows the proposed method provides satisfactory results on SemEVAL-2010 dataset.
机译:审稿人分配问题(RAP)的定义是为提案分配最合适的专家的问题。对于任何研究组织而言,这都是非常重要的任务。在文献中,已经提出了许多解决RAP的解决方案,但大多数解决方案都不涉及与问题相关的不精确性。在本文中,已经提出了一种新颖的方法,用于根据合法专家的先前历史和领域专业知识将其分配给合法专家。为此,我们首先通过构建模糊图并对其应用模糊图中心性度量,从作者提交的论文和审稿人的已发表文章中提取最重要的词。然后,我们为所选关键字及其权重(针对作者的论文和审稿人的出版物)创建了模糊集。之后,使用WordNet来找到作者论文审阅者论文所代表的模糊集之间的距离。最后,在集合上应用模糊扩展原理,以选出前三名专家对提案进行审查。实验表明,该方法在SemEVAL-2010数据集上提供了令人满意的结果。

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