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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.13755 |
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| _version_ | 1866909151756550144 |
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| author | Shishehgarkhaneh, Milad Baghalzadeh Moehler, Robert C. Fang, Yihai Hijazi, Amer A. Aboutorab, Hamed |
| author_facet | Shishehgarkhaneh, Milad Baghalzadeh Moehler, Robert C. Fang, Yihai Hijazi, Amer A. Aboutorab, Hamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13755 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia Shishehgarkhaneh, Milad Baghalzadeh Moehler, Robert C. Fang, Yihai Hijazi, Amer A. Aboutorab, Hamed Computation and Language 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. |
| title | Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.13755 |