Japanese Author Attribution Using BERT Finetuning with Stylometric Features

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AYZ8DATAH" target="_blank" >RIV/00216208:11320/26:YZ8DATAH - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-981-96-5123-8_20" target="_blank" >http://dx.doi.org/10.1007/978-981-96-5123-8_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-5123-8_20" target="_blank" >10.1007/978-981-96-5123-8_20</a>

Result language

angličtina

Original language name

Japanese Author Attribution Using BERT Finetuning with Stylometric Features

Original language description

This study investigates author attribution (AA) in Japanese texts through fine-tuning the pre-trained BERT model “cl-tohoku/bert-large-japanese-v2” with Japanese-specific stylometric features. Experiments explored combinations of these features with classifiers such as LR, SVM, and RF, across varying author counts from 5 to 75. Focusing solely on native Japanese compositions, the study utilized the “Composition Bilingual Database” from the National Institute for Japanese Language and Linguistics to maintain linguistic consistency. The BERT model combined with LR achieved the highest accuracy of 96.3% for 5 authors, demonstrating deep learning’s potential in Japanese AA. However, high-dimensional stylistic features introduced noise when integrated, highlighting challenges in feature alignment. Future work will explore advanced non-linear models like XGBoost, LightGBM, and CatBoost for improved feature integration, and low-resource classification methods such as prototypical networks to enhance performance without extensive dataset expansion. Additionally, further testing of alternative Japanese pre-trained language models will be conducted to capture linguistic nuances more effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Commun. Comput. Info. Sci.

ISBN

978-981-96-5122-1

ISSN

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e-ISSN

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Number of pages

15

Pages from-to

293-307

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

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Event location

Beijing

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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