Omer Anjum


2019

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PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
Omer Anjum | Hongyu Gong | Suma Bhat | Wen-Mei Hwu | JinJun Xiong
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers. Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden. Prior approaches including bag-of-words model and probabilistic topic model are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. Our approach, the common topic model, jointly models the topics common to the submission and the reviewer’s profile while relying on abstract topic vectors. Experiments and insightful evaluations on two datasets demonstrate that the proposed method achieves consistent improvements compared to the state-of-the-art.