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Wang LH, Liu XM, Liu Y, Li HR, Liu JQI, Yang LB. Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network. PLoS One 2023; 18:e0292004. [PMID: 37812633 PMCID: PMC10561837 DOI: 10.1371/journal.pone.0292004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/10/2023] [Indexed: 10/11/2023] Open
Abstract
Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model's comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network's proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matrix to capture a broader range of neighboring node information, thereby enhancing the network's ability to handle complex relationships. Lastly, we utilize the PTM-MFGCN to achieve the extraction of emergency entity relationships in water diversion project, thus constructing a knowledge graph for water diversion emergency management. The experimental results demonstrate that PTM-MFGCN exhibits improvements of 2.84% in accuracy, 4.87% in recall, and 5.18% in F1 score, compared to the baseline model. Relevant studies can effectively enhance the efficiency and capability of emergency management, mitigating the impact of unforeseen events on engineering safety.
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Affiliation(s)
- Li Hu Wang
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
| | - Xue Mei Liu
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
- Collaborative Innovation Centre for Efficient Utilization of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
| | - Yang Liu
- Collaborative Innovation Centre for Efficient Utilization of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
| | - Hai Rui Li
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
| | - Jia QI Liu
- Collaborative Innovation Centre for Efficient Utilization of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
| | - Li Bo Yang
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China
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Wan B, Dong S, Chu D, Li H, Liu Y, Fu J, Fang F, Li S, Zhou D. A deep neural network model for coreference resolution in geological domain. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Savci P, Das B. Comparison of pre-trained language models in terms of carbon emissions, time and accuracy in multi-label text classification using AutoML. Heliyon 2023; 9:e15670. [PMID: 37187909 PMCID: PMC10176029 DOI: 10.1016/j.heliyon.2023.e15670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
Since Turkish is an agglutinative language and contains reduplication, idiom, and metaphor words, Turkish texts are sources of information with extremely rich meanings. For this reason, the processing and classification of Turkish texts according to their characteristics is both time-consuming and difficult. In this study, the performances of pre-trained language models for multi-text classification using Autotrain were compared in a 250 K Turkish dataset that we created. The results showed that the BERTurk (uncased, 128 k) language model on the dataset showed higher accuracy performance with a training time of 66 min compared to the other models and the CO2 emission was quite low. The ConvBERTurk mC4 (uncased) model is also the best-performing second language model. As a result of this study, we have provided a deeper understanding of the capabilities of pre-trained language models for Turkish on machine learning.
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Affiliation(s)
- Pinar Savci
- Arçelik A.Ş. Karaağaç Caddesi 2-6, Sütlüce Beyoğlu 34445 Istanbul, Turkey
| | - Bihter Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
- Corresponding author.
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