Consistency Analysis of NLP Approaches for a Conference Reviewer-Manuscript Match-Making System

Nishith Kotak, Anil K. Roy, Sourish Dasgupta, Tirthankar Ghosal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Peer-review process is an important part of scholarly communication. The quality of a conference also depends on its peer-review process. The selection of a competent reviewer to review a submitted manuscript in a conference, is a crucial facet of a peer-review process. This selection relies on the adopted match-making approach along with the constraint optimization reviewer allocation algorithm. The match-making approach needs to be consistent with its decision of selection and allocation of the reviewers. In this work, we proposed a framework for evaluating the consistency of various standard NLP approaches that are used for match-making process in a conference. The consistency analysis was performed over a real multi-tracked conference organized in 2019. We showed that the Contextual Neural Topic Modeling (CNTM) with word embedding technique was most consistence among all the 13 approaches that we chose to analyze.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441759
DOIs
StatePublished - 2021
Externally publishedYes
Event18th IEEE India Council International Conference, INDICON 2021 - Guwahati, India
Duration: Dec 19 2021Dec 21 2021

Publication series

NameProceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021

Conference

Conference18th IEEE India Council International Conference, INDICON 2021
Country/TerritoryIndia
CityGuwahati
Period12/19/2112/21/21

Keywords

  • CNTM
  • Consistency Analysis
  • Keyword extraction approach
  • NLP
  • Reviewer-Manuscript Match-Making
  • Semantics Analysis
  • Topic Modeling approach
  • Transformer based embedding approach

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