  With the rapid expansion of academic literature and the proliferation of
preprints, researchers face growing challenges in manually organizing and
labeling large volumes of articles. The NSLP 2024 FoRC Shared Task I addresses
this challenge organized as a competition. The goal is to develop a classifier
capable of predicting one of 123 predefined classes from the Open Research
Knowledge Graph (ORKG) taxonomy of research fields for a given article.This
paper presents our results. Initially, we enrich the dataset (containing
English scholarly articles sourced from ORKG and arXiv), then leverage
different pre-trained language Models (PLMs), specifically BERT, and explore
their efficacy in transfer learning for this downstream task. Our experiments
encompass feature-based and fine-tuned transfer learning approaches using
diverse PLMs, optimized for scientific tasks, including SciBERT, SciNCL, and
SPECTER2. We conduct hyperparameter tuning and investigate the impact of data
augmentation from bibliographic databases such as OpenAlex, Semantic Scholar,
and Crossref. Our results demonstrate that fine-tuning pre-trained models
substantially enhances classification performance, with SPECTER2 emerging as
the most accurate model. Moreover, enriching the dataset with additional
metadata improves classification outcomes significantly, especially when
integrating information from S2AG, OpenAlex and Crossref. Our best-performing
approach achieves a weighted F1-score of 0.7415. Overall, our study contributes
to the advancement of reliable automated systems for scholarly publication
categorization, offering a potential solution to the laborious manual curation
process, thereby facilitating researchers in efficiently locating relevant
resources.
