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Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia

arXiv 2311.13755 ↗First posted 23 Nov 2023cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 126 references that are in the map. First posted on arXiv 23 Nov 2023.

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31 of 126 references.

References · 31

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  7. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  8. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  9. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  10. 2018Deep contextualized word representationscited by 2,290
  11. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  12. 2021Transformer in Transformercited by 633
19 more
  1. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
  2. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  3. 2023GPT-NER: Named Entity Recognition via Large Language Modelscited by 160
  4. 2018Dissecting Contextual Word Embeddings: Architecture and Representationcited by 146
  5. 2017Natural Language Processing: State of The Art, Current Trends and Challengescited by 114
  6. 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
  7. 2019Word Embeddings: A Surveycited by 45
  8. 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 45
  9. 2022Wojood: Nested Arabic Named Entity Corpus and Recognition using BERTcited by 33
  10. 2021A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizescited by 20
  11. 2022KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reportscited by 19
  12. 2020A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Eventscited by 8
  13. 2020Accelerated Large Batch Optimization of BERT Pretraining in 54 minutescited by 7
  14. 2021Translational NLP: A New Paradigm and General Principles for Natural Language Processing Researchcited by 7
  15. 2022VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognitioncited by 5
  16. 2021Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRCcited by 4
  17. 2020BioNerFlair: biomedical named entity recognition using flair embedding and sequence taggercited by 2
  18. 2022LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognitioncited by 1
  19. 2023Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and Englishcited by 1

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