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.

翻译：澳大利亚建筑业以其错综复杂的供应链和对多种风险的脆弱性为特征。因此，有效的供应链风险管理变得至关重要。本文采用不同的Transformer模型，并针对澳大利亚建筑供应链风险管理语境进行命名实体识别训练。利用命名实体识别，Transformer模型能够识别并分类新闻文章中与风险相关的特定实体，从而提供对供应链脆弱性的详细洞察。通过使用不同的Transformer模型分析新闻文章，我们可以提取与澳大利亚建筑领域特定风险分类体系相关的实体和见解。本研究强调了自然语言处理驱动的解决方案（如Transformer模型）在革新特定地理媒体语境下的建筑供应链风险管理中的潜力。