Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia

TL;DR

This paper explores the use of transformer-based models for Named Entity Recognition to identify risk-related entities in news articles, enhancing supply chain risk management in Australia's construction industry.

Contribution

It introduces the application of transformer models for NER in Australian construction supply chain risk management, demonstrating NLP's potential in this domain.

Findings

Transformer models effectively identify risk entities in news articles.

NER insights improve understanding of supply chain vulnerabilities.

NLP methods can revolutionize construction risk management.

Abstract

The construction industry in Australia is characterized by its intricate supply chains and vulnerability to myriad risks. As such, effective supply chain risk management (SCRM) becomes imperative. This paper employs different transformer models, and train for Named Entity Recognition (NER) in the context of Australian construction SCRM. Utilizing NER, transformer models identify and classify specific risk-associated entities in news articles, offering a detailed insight into supply chain vulnerabilities. By analysing news articles through different transformer models, we can extract relevant entities and insights related to specific risk taxonomies local (milieu) to the Australian construction landscape. This research emphasises the potential of NLP-driven solutions, like transformer models, in revolutionising SCRM for construction in geo-media specific contexts.