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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Khouya, Nabila
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Computer Science & IT Engineering

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Interdisciplinary Analysis of Machine Learning Applications: Focus on Intent Classification Khouya, Nabila; Retbi, Asmaâ; Bennani, Samir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6899

Abstract

Given the rapid growth of machine learning publications on platforms such as arXiv, there is a need for systematic approaches to understand their objectives and contributions. This study aimed to analyze scientific intentions across domains, identify research trends, and evaluate the impact of external contextual enrichment on automatic intent classification. We perform a cross-domain comparison of research objectives, methodological designs, and application scenarios in machine learning publications, focusing on computer science and biology. We propose IntentBERT-Wiki, an enhanced BERT model enriched with contextual knowledge from Wikipedia, designed for intent classification in scientific documents. Our dataset comprises annotated sentences extracted from arXiv articles, categorized according to established rhetorical role taxonomies. The model’s performance is evaluated using standard classification metrics and compared to a baseline BERT model. Experimental results show that IntentBERT-Wiki achieves F1-scores of 95.9% in computer science and 87.4% in biology, with corresponding accuracies of 96.5% and 91.4%, outperforming the baseline. These findings demonstrate that Wikipedia-based contextual enrichment can significantly improve intent classification accuracy, enhance the organization of academic discourse, and facilitate cross-domain knowledge transfers. This study contributes to the understanding of how machine learning research is framed across disciplines and provides a scalable framework for scientific content analysis.
GraphiBERT-ML: A Knowledge-Enhanced NER Approach for Cross-Domain Comparative Analysis of Machine Learning Literature Khouya, Nabila; Retbi, Asmaâ; Bennani, Samir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7160

Abstract

The exponential growth of scientific literature on platforms such as arXiv presents a major challenge in identifying and comparing key contributions to machine learning across diverse academic domains. To address this, we propose GraphiBERT-ML, a knowledge-enhanced extension of BERT that integrates semantic embeddings extracted from DBpedia to improve named entity recognition (NER) in scientific articles. To the best of our knowledge, this study presents the first knowledge-enhanced NER model that explicitly integrates DBpedia-based embeddings for large-scale cross-domain scientific analyses. The model was evaluated on a cross-domain dataset spanning eight fields, including computer science, physics, biology, finance, and economics. Experimental results show that GraphiBERT-ML achieves its highest performance in computer science, with an accuracy of 0.9372, an F1-score of 0.9368, and a precision of 0.9376. Physics and mathematics also demonstrate strong performance (F1-scores of 0.9115 and 0.8970), while more heterogeneous domains such as biology and finance show lower scores (F1-scores of 0.7946 and 0.7872), reflecting the complexity and variability of their terminology. Across all domains, GraphiBERT-ML consistently outperformed the baseline BERT model, confirming the benefit of external knowledge integration for scientific NER. These findings highlight domain-specific challenges in entity extraction and demonstrate the potential of knowledge-augmented models to advance cross-disciplinary analysis of machine learning research.
Co-Authors Bennani, Samir Retbi, Asmaa