TY - GEN
T1 - A Consistency Analysis of Different NLP Approaches for Reviewer-Manuscript Matchmaking
AU - Kotak, Nishith
AU - Roy, Anil K.
AU - Dasgupta, Sourish
AU - Ghosal, Tirthankar
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Selecting a potential reviewer to review a manuscript, submitted at a conference is a crucial task for the quality of a peer-review process that ultimately determines the success and impact of any conference. The approach adopted to find the potential reviewer needs to be consistent with its decision of allocation. In this work, we propose a framework for evaluating the reliability of different NLP approaches that are implemented for the match-making process. We bring various algorithmic approaches from different paradigms and an existing system Erie, implemented in IEEE INFOCOM conference, on a common platform to study their consistency of predicting the set of the potential reviewers, for a given manuscript. The consistency analysis has been performed over an actual multi-track conference organized in 2019. We conclude that Contextual Neural Topic Modeling (CNTM) with a balanced combinatorial optimization technique showed better consistency, among all the approaches we choose to study.
AB - Selecting a potential reviewer to review a manuscript, submitted at a conference is a crucial task for the quality of a peer-review process that ultimately determines the success and impact of any conference. The approach adopted to find the potential reviewer needs to be consistent with its decision of allocation. In this work, we propose a framework for evaluating the reliability of different NLP approaches that are implemented for the match-making process. We bring various algorithmic approaches from different paradigms and an existing system Erie, implemented in IEEE INFOCOM conference, on a common platform to study their consistency of predicting the set of the potential reviewers, for a given manuscript. The consistency analysis has been performed over an actual multi-track conference organized in 2019. We conclude that Contextual Neural Topic Modeling (CNTM) with a balanced combinatorial optimization technique showed better consistency, among all the approaches we choose to study.
KW - Consistency analysis
KW - Reviewer-manuscript matching
KW - Semantics analysis
UR - http://www.scopus.com/inward/record.url?scp=85121921234&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-91669-5_22
DO - 10.1007/978-3-030-91669-5_22
M3 - Conference contribution
AN - SCOPUS:85121921234
SN - 9783030916688
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 277
EP - 287
BT - Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings
A2 - Ke, Hao-Ren
A2 - Lee, Chei Sian
A2 - Sugiyama, Kazunari
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021
Y2 - 1 December 2021 through 3 December 2021
ER -