別紙41 (課程博士(英文))Date of submjssion (month day, year)

January 6,2023

Department ofComputer science

StudentlD Numberand Engineering
Pplicarlt's name

D199304asa加ShiTsuchiyaSupervisors

Orihide lくitaoka

Setio Basuki

Abstract (Doctor)
Title ofThesis

Ihe crucial Role of citation Functjons in The Techn010gy・assisted peer Review

Approx.80o words

This research aims to develop a citation functions'based prediction method ofPaper quality to support Techn010gy'assisted peer Review (TAPR). The prediction
method is intended t01'educe the review burden which becolnes a 仇'iticalissue intoday's paper submission process. since the review burden problem haS 晉ainedmuch attention, many works have developed the TAPR system to handle thisissue. However, most of existin号 Works were created by involvin牙 reviewers'
Comments which is considered unapplicable for reducing the review burden
Addressin牙 this issue, this research proposes a prediction method to estimate thePaper quality dependin套 only on the paper itseH. The estimator ofpaper quality
Used in this paper is citation functions which represent the reason why author ofresearch paper cites previous works. Moreover, the citation functions present thePosition ofthe proposed research in wide、rangin牙 literature, understand thebroad view' of the 号iven research topics, indicates the novelty ofthe proposed
research, and estimate the quality ofthe proposed research
The cha11enge for estimatin目 the paper quality usin牙 Citation functions wi11
depend on whether the labels are representative enough to capture aⅡ Potential
Citation roles in the fU11text ofresearch paper. Handlin晉 drawbacks ofcurrent
available scheme ofcitation functions that have a sma11 number ofcitation

instances, few types of labels, and suffer froln lack of research variety, thisresearch proposes a new' 1abelin牙 Scheme of citation functions coverin号 m.ulti、field
Computer science d0皿ains consistin牙 of 5 Coarse labels and 21 fine、牙rained labels
The annotation exP田'iments on the proposed scheme achieved cohen'S Kappa
Values of o.85 for coarse labels and o.71 for fine・牙rained labels. The schem.e isthen used to construct a lar牙e dataset ofcitation functions using a semiautomatic
approach which f0110ws two classification sta目es, i.e., filterin晉 and fine"牙rainedAdoptin牙 the Active Learning (AL) techniques usin目 less than halfoftrainin号
dataset, the Bidirectional Encoder Representations from Transformers (BERT)、
based AL in the filtering sta号e and sciBERT・based AL in the fine・今rained sta今ereached the accuracies of o.90 and o.81, respectively. Fina11y, this researchreleased the largest dataset consistin牙 of l,840,815 instances
The prediction lnethod for TAPR, which covers two classification tasks and one
re牙ression task, is developed based on the proposed scheme and the best lnodels

to create the dataset. X入7hile the classification tasks focus on predictin牙 the final

review decision (accepted、rejected) and estimating the paper quality (旦ood・poor),
the re今ression task is used to predict the peer review scores. Both classification

and re号ression are implemented usin晉 three features i.e., citin牙 Sentence featuresdeveloped based on labeling sche皿e of citation functions, re今Ular sentence
features created by applyin晉 the label of citation functions to non・citation text,
reference'based features constructed by identifyin今 the source of citations. The
Classification experiments on the lnternational conference on Learning

Representations qcLR) 2017'2020 showed that the proposed methods are more
effective in the good'poor task compared to the accepted・rejected task bydemonstratin晉 the best accuracy of o.75 and o.73, respectively. obtainin号 as
many good papers as possible in the 目ood、poor task, this research reached a

Satisfyin牙 reca110f o.99 by usin目 only the citin今 Sentence features. The re今ression
experiments indicate that the best result in predictin目 the avera号e review' scoreis higher than in the individualreview sC仇.e by showing RMSE of l.34 and l.71,
Subsequently

As mentioned above■'eachin牙 as hi牙h reca11 as possible is important to get as
many good papers as possible, which is more reasonable and applicable for
Supportin今 the editor to filter the sublnitted manuscripts.1nterestingly, thishi晉hest reca11 Was reached by using only the citing sentences feature. Theseresults prove the hypothesis ofthis research about the crucial role of citation
functions in the manuscript