Chinese Address Recognition Method Based on Multi-Feature Fusion
A place name is a textual identification of a specific spatial location by people and is an important carrier of geographical information. The recognition of Chinese place names is of great importance in information retrieval and event extraction. The traditional approach is to transform the recogni...
| 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/9916249/ |
| _version_ | 1797243431208091648 |
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| author | Yansong Wang Meng Wang Chaoling Ding Xinghua Yang Jian Chen |
| author_facet | Yansong Wang Meng Wang Chaoling Ding Xinghua Yang Jian Chen |
| author_sort | Yansong Wang |
| collection | DOAJ |
| description | A place name is a textual identification of a specific spatial location by people and is an important carrier of geographical information. The recognition of Chinese place names is of great importance in information retrieval and event extraction. The traditional approach is to transform the recognition of Chinese place names into a sequential annotation problem, with commonly used classification models such as support vector machines and conditional random fields. In this paper, Chinese address recognition is converted into a sequential annotation task, and a multi-feature fusion approach to Chinese address recognition is proposed. A deep learning network architecture model based on the fusion of character, word, and address features is constructed to convert characters, words, and their features into vector representations; finally, the sequential annotation of sentences is performed by CRF to achieve the recognition and extraction of address information. On the autonomously constructed dataset, the proposed method MFBL (Multi-Feature-BiLSTM) improves in accuracy by 4 to 10 percentage points compared to other methods, demonstrating that the MFBL model has better performance in the address recognition task. |
| first_indexed | 2024-04-24T18:55:00Z |
| format | Article |
| id | doaj.art-54ac47bdca0143d4abd8090bcf52c18f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:55:00Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-54ac47bdca0143d4abd8090bcf52c18f2024-03-26T17:43:19ZengIEEEIEEE Access2169-35362022-01-011010890510891310.1109/ACCESS.2022.32139769916249Chinese Address Recognition Method Based on Multi-Feature FusionYansong Wang0Meng Wang1Chaoling Ding2Xinghua Yang3Jian Chen4https://orcid.org/0000-0003-0593-8068Chery HuiYin Motor Finance Service Company Ltd., Wuhu, ChinaChery HuiYin Motor Finance Service Company Ltd., Wuhu, ChinaChery HuiYin Motor Finance Service Company Ltd., Wuhu, ChinaChery HuiYin Motor Finance Service Company Ltd., Wuhu, ChinaYangtze River Delta Information Intelligence Innovation Research Institute, Wuhu, ChinaA place name is a textual identification of a specific spatial location by people and is an important carrier of geographical information. The recognition of Chinese place names is of great importance in information retrieval and event extraction. The traditional approach is to transform the recognition of Chinese place names into a sequential annotation problem, with commonly used classification models such as support vector machines and conditional random fields. In this paper, Chinese address recognition is converted into a sequential annotation task, and a multi-feature fusion approach to Chinese address recognition is proposed. A deep learning network architecture model based on the fusion of character, word, and address features is constructed to convert characters, words, and their features into vector representations; finally, the sequential annotation of sentences is performed by CRF to achieve the recognition and extraction of address information. On the autonomously constructed dataset, the proposed method MFBL (Multi-Feature-BiLSTM) improves in accuracy by 4 to 10 percentage points compared to other methods, demonstrating that the MFBL model has better performance in the address recognition task.https://ieeexplore.ieee.org/document/9916249/Address recognitionnamed entity recognitiondeep learningconditional random fields |
| spellingShingle | Yansong Wang Meng Wang Chaoling Ding Xinghua Yang Jian Chen Chinese Address Recognition Method Based on Multi-Feature Fusion IEEE Access Address recognition named entity recognition deep learning conditional random fields |
| title | Chinese Address Recognition Method Based on Multi-Feature Fusion |
| title_full | Chinese Address Recognition Method Based on Multi-Feature Fusion |
| title_fullStr | Chinese Address Recognition Method Based on Multi-Feature Fusion |
| title_full_unstemmed | Chinese Address Recognition Method Based on Multi-Feature Fusion |
| title_short | Chinese Address Recognition Method Based on Multi-Feature Fusion |
| title_sort | chinese address recognition method based on multi feature fusion |
| topic | Address recognition named entity recognition deep learning conditional random fields |
| url | https://ieeexplore.ieee.org/document/9916249/ |
| work_keys_str_mv | AT yansongwang chineseaddressrecognitionmethodbasedonmultifeaturefusion AT mengwang chineseaddressrecognitionmethodbasedonmultifeaturefusion AT chaolingding chineseaddressrecognitionmethodbasedonmultifeaturefusion AT xinghuayang chineseaddressrecognitionmethodbasedonmultifeaturefusion AT jianchen chineseaddressrecognitionmethodbasedonmultifeaturefusion |