[1] An Empirical Comparison and Ensemble Learning Methods of BERT Models on Authorship Attribution, Taisei KANDA, Mingzhe JIN. Journal of Japan Society of Information and Knowledge, 34(3), 244-255, https://doi.org/10.2964/jsik_2024_022, 2024/9/30.
[2] Can we spot fake public comments generated by ChatGPT(-3.5, -4)?: Japanese stylometric analysis exposes emulation created by one-shot learning. Wataru ZAITSU, Mingzhe JIN, Shunichi ISHIHARA, Satoru TSUGE, Mitsuyuki INABA. Public Library of Science (PLOS), 19(3) e0299031, DIO: 10.1371/journal.pone.0299031, 2024/03/13
[3] A CORPUS-BASED STYLISTIC ANALYS DID THE NOVELIST MINAE MIZUMURA ACHIEVE HER LITERARY GOAL, Guangwei LI, Mingzhe JIN, Yasuko NAKAMURA. PSYCHOLOGIA, 65(2), 273–283. https://doi.org/10.2117/psysoc.2023-B038, 2024/02
[4] QUANTITATIVE ANALYSIS OF THE CHARACTERISTICS AND HISTORICAL TRANSITION OF EDOGAWA RAMPO’S WORKS, Tetsuya YAMAMOTO, Yasuko NAKAMURA, Hideki OHIRA, Mingzhe JIN. PSYCHOLOGIA, 65(2), 284-295. https://doi.org/10.2117/psysoc.2023-b036, 2024/02.
[5] Analysis of stock market movement prediction with pre-trained language model), Jinyang Li, Mingzhe JIN, Hiroshi YADOHISHA. Artificial Intelligence Frontier, ISSN: 29580-1479, 1(2), 26-39, https://doi.org/10.55375/aif.2023.2.3, 2023/9/15.
[6] Authorship Attribution Using the Nucleus BunSetsu as Stylometric Features in Japanese Writings, Yejia LIU, Mingzhe JIN. Bulletin of Data Analysis of Japanese Classification Society, 12(1), 33-36, https://doi.org/10.32146/bdajcs.12.33, 2023/09.
[7] Improving the Performance of Feature Selection Methods with Low-Sample-Size Data, Wanwan ZHENG, Mingzhe JIN.The Computer Journal, 66(7), 1664-1686, https://doi.org/10.1093/comjnl/bxac033, 2023/7/9.
[8] Distinguishing ChatGPT(3.5, 4)-generated and human-written papers through Japanese stylometric analysis, Zaitsu. WATARU, Mingzhe. JIN. Public Library of Science (PLOS ONE) 18(8) , https://doi.org/10.1371/journal.pone.0288453, 2023/8/9,
[9] Is Word-length Inaccurate for Authorship Attribution? , Wanwan. ZHENG, Mingzhe. JIN. DSH: Digital Scholarship in the Humanities, 38(2), 875-890, https://doi.org/10.1093/llc/fqac067, 2022/11/01
[10] A Review on Authorship Attribution in Text Mining, Wanwan. ZHENG, Mingzhe. JIN. WIREs Computational Statistics, 15(2). 2022/4/4 accepted. First published: 2022/4/22, https://doi.org/10.1002/wics.1584
[11] Authorship Attribution in the Multi-genre Mingled Corpus, Yejia Liu, Mingzhe JIN. Bulletin of Data Analysis of Japanese Classification Society(in Japanese)， 11 (1), 1-14, https://doi.org/10.32146/bdajcs.11.1, 2022/3/29.
[12] Statistical Modeling and Analysis of Diachronic Changes in Sentence-final Expressions in Modern Novels， Guangwei LI, Mingzhe JIN. Mathematical Linguistics(in Japanese)，2022/06.
[13] A Corpus-based Approach to Explore the Stylistic Peculiarity of Koji Uno's Postwar Works， Xueqin LIU, Mingzhe JIN. DSH: Digital Scholarship in the Humanities. 37(1)，168–184，April 2022/3/23，https://doi.org/10.1093/llc/fqab029，
[14] The Effectiveness of the Maximal Information Coefficients in Real-World Classification Tasks， Yanru CHEN, Wanwan ZHENG, Mingzhe JIN. The Harris science review of Doshisha University(in Japanese)，62(3)，17-24. 2021/10/31
[15] Discriminant Analysis for Corporate Bankruptcy using Financial Numerical and Textual Data, Limeng XU, Mingzhe JIN. Bulletin of Data Analysis of Japanese Classification Society(in Japanese), 10 (1)，45–57.https://doi.org/10.32146/bdajcs.10.45. 2021
[16] Modeling Analysis of Diachronic Changes in Auxiliary Words in Novels, Guangwei LI, Mingzhe JIN. Journal of Japan Society of Information and Knowledge(in Japanese)，31(3)，371-383. 2021/01/29
