Shishehgarkhaneh, Milad B, Chan, Melissa ORCID: 0000-0003-1589-6850, Moehler, Robert C, Fang, Yihai, Hijazi, Amer A and Aboutorab, Hamed (2024) Named Entity Recognition in Construction Supply Chain Risk Management Using Transformer-Based Models and Genetic Algorithm-Driven Hyperparameter Tuning. In: 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), 25 Jul 2024 - 27 Jul 2024, Sydney, Australia.

Abstract

The construction industry faces significant supply chain risks, necessitating effective management strategies. This paper presents a novel approach using transformer-based models for Named Entity Recognition (NER) to identify risk-related entities within construction supply chain management (SCRM) from news articles. It specifically explores the efficacy of two transformer models, BERT and DeBERTa, and employs Genetic Algorithms (GAs) for optimizing model hyperparameters. This research underscores the transformative potential of NLP-driven solutions in enhancing SCRM, particularly within the unique context of the Australian construction industry. The findings highlight the importance of precision in entity recognition for effective SCRM and demonstrate the superiority of DeBERTa in precision-focused tasks, making it a promising tool for practitioners in this field.

Search Google Scholar

Repository staff login