Advancing Scientific Text Classification: Fine-Tuned Models with Dataset Expansion and Hard-Voting

• URL: http://arxiv.org/abs/2504.19021v1
• Date: Sat, 26 Apr 2025 21:06:49 GMT
• Title: Advancing Scientific Text Classification: Fine-Tuned Models with Dataset Expansion and Hard-Voting
• Authors: Zhyar Rzgar K Rostam, Gábor Kertész,
• Abstract summary: BERT, SciBERT, BioBERT, and BlueBERT are fine-tuned on the Web of Science (WoS-46985) dataset for scientific text classification.<n>We augment the dataset by executing seven targeted queries in the WoS database, retrieving 1,000 articles per category aligned with WoS-46985's main classes.<n>Fine-tuning on the expanded dataset with dynamic learning rates and early stopping significantly boosts classification accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficient text classification is essential for handling the increasing volume of academic publications. This study explores the use of pre-trained language models (PLMs), including BERT, SciBERT, BioBERT, and BlueBERT, fine-tuned on the Web of Science (WoS-46985) dataset for scientific text classification. To enhance performance, we augment the dataset by executing seven targeted queries in the WoS database, retrieving 1,000 articles per category aligned with WoS-46985's main classes. PLMs predict labels for this unlabeled data, and a hard-voting strategy combines predictions for improved accuracy and confidence. Fine-tuning on the expanded dataset with dynamic learning rates and early stopping significantly boosts classification accuracy, especially in specialized domains. Domain-specific models like SciBERT and BioBERT consistently outperform general-purpose models such as BERT. These findings underscore the efficacy of dataset augmentation, inference-driven label prediction, hard-voting, and fine-tuning techniques in creating robust and scalable solutions for automated academic text classification.

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