@inproceedings{anjum-etal-2019-pare,
title = "{P}a{R}e: A Paper-Reviewer Matching Approach Using a Common Topic Space",
author = "Anjum, Omer and
Gong, Hongyu and
Bhat, Suma and
Hwu, Wen-Mei and
Xiong, JinJun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1049",
doi = "10.18653/v1/D19-1049",
pages = "518--528",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
%A Anjum, Omer
%A Gong, Hongyu
%A Bhat, Suma
%A Hwu, Wen-Mei
%A Xiong, JinJun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F anjum-etal-2019-pare
%X 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.
%R 10.18653/v1/D19-1049
%U https://aclanthology.org/D19-1049
%U https://doi.org/10.18653/v1/D19-1049
%P 518-528
Markdown (Informal)
[PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space](https://aclanthology.org/D19-1049) (Anjum et al., EMNLP-IJCNLP 2019)
ACL
- Omer Anjum, Hongyu Gong, Suma Bhat, Wen-Mei Hwu, and JinJun Xiong. 2019. PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 518–528, Hong Kong, China. Association for Computational Linguistics.