Transformer-based Named Entity Recognition in Construction Supply Chain
  Risk Management in Australia
Milad Baghalzadeh Shishehgarkhaneh Robert C. Moehler Yihai Fang Amer A. Hijazi Hamed Aboutorab
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.