A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea
Toponymic entity recognition is currently a critical research hotspot in knowledge graphs. Under the guidance of the national ancient book protection policy and the promotion of the wave of digital humanities research, this paper proposes a toponymic entity recognition model (ALBERT-Conv1D-BiLSTM-CR...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/1/4 |
| _version_ | 1797358934987636736 |
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| author | Yinwei Wei Yihong Li Xiaoyi Zhou |
| author_facet | Yinwei Wei Yihong Li Xiaoyi Zhou |
| author_sort | Yinwei Wei |
| collection | DOAJ |
| description | Toponymic entity recognition is currently a critical research hotspot in knowledge graphs. Under the guidance of the national ancient book protection policy and the promotion of the wave of digital humanities research, this paper proposes a toponymic entity recognition model (ALBERT-Conv1D-BiLSTM-CRF) based on the fusion of a pre-trained language model and local features to address the problems of toponymic ambiguity and the differences in ancient and modern grammatical structures in the field of the Genglubu. This model extracts global features with the ALBERT module, fuses global and local features with the Conv1D module, performs sequence modeling with the BiLSTM module to capture deep semantics and long-distance dependency information, and finally, completes sequence annotation with the CRF module. The experiments show that while taking into account the computational resources and cost, this improved model is significantly improved compared with the benchmark model (ALBERT-BiLSTM-CRF), and the precision, recall, and F1 are increased by 0.74%, 1.28%, and 1.01% to 98.08%, 96.67%, and 97.37%, respectively. The model achieved good results in the field of Genglubu. |
| first_indexed | 2024-03-08T15:09:21Z |
| format | Article |
| id | doaj.art-600c021fa8d2487bb0e63355669a5eab |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T15:09:21Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-600c021fa8d2487bb0e63355669a5eab2024-01-10T14:54:04ZengMDPI AGElectronics2079-92922023-12-01131410.3390/electronics13010004A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China SeaYinwei Wei0Yihong Li1Xiaoyi Zhou2School of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaToponymic entity recognition is currently a critical research hotspot in knowledge graphs. Under the guidance of the national ancient book protection policy and the promotion of the wave of digital humanities research, this paper proposes a toponymic entity recognition model (ALBERT-Conv1D-BiLSTM-CRF) based on the fusion of a pre-trained language model and local features to address the problems of toponymic ambiguity and the differences in ancient and modern grammatical structures in the field of the Genglubu. This model extracts global features with the ALBERT module, fuses global and local features with the Conv1D module, performs sequence modeling with the BiLSTM module to capture deep semantics and long-distance dependency information, and finally, completes sequence annotation with the CRF module. The experiments show that while taking into account the computational resources and cost, this improved model is significantly improved compared with the benchmark model (ALBERT-BiLSTM-CRF), and the precision, recall, and F1 are increased by 0.74%, 1.28%, and 1.01% to 98.08%, 96.67%, and 97.37%, respectively. The model achieved good results in the field of Genglubu.https://www.mdpi.com/2079-9292/13/1/4toponymic entity recognitionGenglubu corpuspre-trained language modellocal featuredigital humanities |
| spellingShingle | Yinwei Wei Yihong Li Xiaoyi Zhou A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea Electronics toponymic entity recognition Genglubu corpus pre-trained language model local feature digital humanities |
| title | A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea |
| title_full | A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea |
| title_fullStr | A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea |
| title_full_unstemmed | A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea |
| title_short | A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea |
| title_sort | study on toponymic entity recognition based on pre trained models fused with local features for genglubu in the south china sea |
| topic | toponymic entity recognition Genglubu corpus pre-trained language model local feature digital humanities |
| url | https://www.mdpi.com/2079-9292/13/1/4 |
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