A Joint Model for Hierarchical Nested Information Extraction
During the long-term power construction process, the power dispatching department has saved many notification texts related to adjustment of grid operation mode. There is an urgent need to study named entity recognition techniques to automatically recognize the power equipment and operation mode, in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9770069/ |
| _version_ | 1817986575215099904 |
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| author | Ruyang Yin Zhencheng Zhou Zonghe Gao |
| author_facet | Ruyang Yin Zhencheng Zhou Zonghe Gao |
| author_sort | Ruyang Yin |
| collection | DOAJ |
| description | During the long-term power construction process, the power dispatching department has saved many notification texts related to adjustment of grid operation mode. There is an urgent need to study named entity recognition techniques to automatically recognize the power equipment and operation mode, in order to support automatic verification of grid operation mode. By analyzing the characteristics of notification texts, a classification method of hierarchical nested named entities is proposed for the first time in power domain. The entities are divided into two layers with nested relationships, and the corpus of grid operation mode is constructed. We further propose a joint model based on character-word feature fusion and attention mechanism. The model is based on the parameter sharing approach for joint recognition of hierarchical nested entities in the corpus and further introduces an attention mechanism to optimize the feature interaction between hierarchical nested entities. In addition, we splice embeddings of characters and words as feature input to obtain richer semantic features. Experimental results show that our model achieves state-of-the-art results. Eventually, the recognition results can be stored as a standardized verification information chain, providing effective data support for automatic verification of the grid operation mode and ensuring safe and stable operation of the grid. |
| first_indexed | 2024-04-14T00:10:58Z |
| format | Article |
| id | doaj.art-eae160ae6ed4496fb396437a7b82d15c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T00:10:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eae160ae6ed4496fb396437a7b82d15c2022-12-22T02:23:20ZengIEEEIEEE Access2169-35362022-01-0110509855099510.1109/ACCESS.2022.31729709770069A Joint Model for Hierarchical Nested Information ExtractionRuyang Yin0https://orcid.org/0000-0003-0971-0167Zhencheng Zhou1https://orcid.org/0000-0001-5808-5447Zonghe Gao2Department of Civil Engineering, Institute of Transport Studies, Monash University, Clayton, VIC, AustraliaState Grid Electric Power Research Institute Company Ltd., Nanjing, ChinaState Grid Electric Power Research Institute Company Ltd., Nanjing, ChinaDuring the long-term power construction process, the power dispatching department has saved many notification texts related to adjustment of grid operation mode. There is an urgent need to study named entity recognition techniques to automatically recognize the power equipment and operation mode, in order to support automatic verification of grid operation mode. By analyzing the characteristics of notification texts, a classification method of hierarchical nested named entities is proposed for the first time in power domain. The entities are divided into two layers with nested relationships, and the corpus of grid operation mode is constructed. We further propose a joint model based on character-word feature fusion and attention mechanism. The model is based on the parameter sharing approach for joint recognition of hierarchical nested entities in the corpus and further introduces an attention mechanism to optimize the feature interaction between hierarchical nested entities. In addition, we splice embeddings of characters and words as feature input to obtain richer semantic features. Experimental results show that our model achieves state-of-the-art results. Eventually, the recognition results can be stored as a standardized verification information chain, providing effective data support for automatic verification of the grid operation mode and ensuring safe and stable operation of the grid.https://ieeexplore.ieee.org/document/9770069/Hierarchical nested named entityjoint modelattention mechanismfeature fusionnamed entity recognition |
| spellingShingle | Ruyang Yin Zhencheng Zhou Zonghe Gao A Joint Model for Hierarchical Nested Information Extraction IEEE Access Hierarchical nested named entity joint model attention mechanism feature fusion named entity recognition |
| title | A Joint Model for Hierarchical Nested Information Extraction |
| title_full | A Joint Model for Hierarchical Nested Information Extraction |
| title_fullStr | A Joint Model for Hierarchical Nested Information Extraction |
| title_full_unstemmed | A Joint Model for Hierarchical Nested Information Extraction |
| title_short | A Joint Model for Hierarchical Nested Information Extraction |
| title_sort | joint model for hierarchical nested information extraction |
| topic | Hierarchical nested named entity joint model attention mechanism feature fusion named entity recognition |
| url | https://ieeexplore.ieee.org/document/9770069/ |
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