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Improved reviewer assignment based on both word and semantic features
Information Retrieval Journal ( IF 1.7 ) Pub Date : 2021-04-02 , DOI: 10.1007/s10791-021-09390-8
Shicheng Tan , Zhen Duan , Shu Zhao , Jie Chen , Yanping Zhang

Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is a common challenge in the academic community. Current word- and semantic-based approaches treat the reviewer assignment problem (RAP) as an information retrieval problem but do not take into account two constraints of the RAP: incompleteness of the reviewer data and interference from nonmanuscript-related papers. In this paper, a word and semantic-based iterative model (WSIM) is proposed to account for the constraints of the RAP by improving the similarity calculations between reviewers and manuscripts. First, we use the improved language model and topic model to extract word features and semantic features to represent reviewers and manuscripts. Second, we use a similarity metric based on the normalized discounted cumulative gain (NDCG) to measure semantic similarity. This metric ignores the probability value (quantitative exact value) of the topic and considers only the ranking (qualitative relevance), thus reducing overfitting to incomplete reviewer data. Finally, we use an iterative model to reduce the interference from nonmanuscript-related papers in the reviewer data. This approach considers the similarity between the manuscript and each of the reviewer’s papers. We evaluate the proposed WSIM on two real datasets and compare its performance to that of seven existing methods. The experimental results show that the WSIM improves the recommendation accuracy by at least 2.5% on the top 20.



中文翻译:

基于单词和语义特征的改进的审阅者分配

从一组候选审稿人中为稿件分配合适的审稿人是学术界的普遍挑战。当前基于单词和语义的方法将审稿人分配问题(RAP)视为信息检索问题,但没有考虑到RAP的两个约束:审稿人数据的不完整和来自与稿件无关的论文的干扰。本文提出了一种基于单词和语义的迭代模型(WSIM),通过改进审稿人与手稿之间的相似度计算来解决RAP的约束。首先,我们使用改进的语言模型和主题模型来提取单词特征和语义特征,以表示审阅者和手稿。第二,我们使用基于归一化贴现累积增益(NDCG)的相似性度量来衡量语义相似性。此度量标准忽略主题的概率值(定量精确值),仅考虑排名(定性相关性),从而减少了对不完整审阅者数据的过度拟合。最后,我们使用迭代模型来减少审稿人数据中来自非手稿相关论文的干扰。这种方法考虑了手稿与审稿人每篇论文之间的相似性。我们在两个真实的数据集上评估了所提出的WSIM,并将其性能与7种现有方法的性能进行了比较。实验结果表明,WSIM在前20名中将推荐准确性提高了至少2.5%。此度量标准忽略主题的概率值(定量精确值),仅考虑排名(定性相关性),从而减少了对不完整审阅者数据的过度拟合。最后,我们使用迭代模型来减少审稿人数据中来自非手稿相关论文的干扰。这种方法考虑了手稿与审稿人每篇论文之间的相似性。我们在两个真实的数据集上评估了所提出的WSIM,并将其性能与7种现有方法的性能进行了比较。实验结果表明,WSIM在前20名中将推荐准确性提高了至少2.5%。此度量标准忽略主题的概率值(定量精确值),仅考虑排名(定性相关性),从而减少了对不完整审阅者数据的过度拟合。最后,我们使用迭代模型来减少审稿人数据中来自非手稿相关论文的干扰。这种方法考虑了手稿与审稿人每篇论文之间的相似性。我们在两个真实的数据集上评估了所提出的WSIM,并将其性能与7种现有方法的性能进行了比较。实验结果表明,WSIM在前20名中将推荐准确性提高了至少2.5%。这种方法考虑了手稿与审稿人每篇论文之间的相似性。我们在两个真实的数据集上评估了所提出的WSIM,并将其性能与7种现有方法的性能进行了比较。实验结果表明,WSIM在前20名中将推荐准确性提高了至少2.5%。这种方法考虑了手稿与审稿人每篇论文之间的相似性。我们在两个真实的数据集上评估了所提出的WSIM,并将其性能与7种现有方法的性能进行了比较。实验结果表明,WSIM在前20名中将推荐准确性提高了至少2.5%。

更新日期:2021-04-04
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