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.

利用命名实体识别 (NER) 结合变压器模型，本研究从新闻文章中提取风险相关的实体和见解，并突出了自然语言处理驱动的解决方案在地理媒体特定情境下革新建筑供应链风险管理 (SCRM) 的潜力。