Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia
Abstract
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.
Keywords
Cite
@article{arxiv.2311.13755,
title = {Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia},
author = {Milad Baghalzadeh Shishehgarkhaneh and Robert C. Moehler and Yihai Fang and Amer A. Hijazi and Hamed Aboutorab},
journal= {arXiv preprint arXiv:2311.13755},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be acceptable