Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia
This work addresses supply chain risk management for the Australian construction industry, but it is incremental as it applies existing transformer models to a new domain-specific dataset.
The paper tackled the problem of identifying and classifying risk-associated entities in news articles for construction supply chain risk management in Australia using transformer-based Named Entity Recognition, achieving detailed insights into supply chain vulnerabilities.
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.