A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language
To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Decoding-enhanced BERT with disentangled attention)-b...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1073 |
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| author | Wenjuan Lu Dongping Ming Xi Mao Jizhou Wang Zhanjie Zhao Yao Cheng |
| author_facet | Wenjuan Lu Dongping Ming Xi Mao Jizhou Wang Zhanjie Zhao Yao Cheng |
| author_sort | Wenjuan Lu |
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| description | To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Decoding-enhanced BERT with disentangled attention)-based natural language spatiotemporal question semantic conversion model. The model first performs semantic encoding on natural language spatiotemporal questions, extracts pre-trained features based on the DeBERTa model, inputs feature vector sequences into BiGRU (Bidirectional Gated Recurrent Unit) to learn text features, and finally obtains globally optimal label sequences through a CRF (Conditional Random Field) layer. Then, based on the encoding results, it performs classification and semantic parsing of spatiotemporal questions to achieve question intent recognition and conversion to Cypher query language. The experimental results show that the proposed DeBERTa-based conversion model NL2Cypher can accurately achieve semantic information extraction and intent understanding in both simple and compound queries when using Chinese corpus, reaching an F1 score of 92.69%, with significant accuracy improvement compared to other models. The conversion accuracy from spatiotemporal questions to query language reaches 88% on the training set and 92% on the test set. The proposed model can quickly and accurately query spatiotemporal data using natural language questions. The research results provide new tools and perspectives for subsequent knowledge graph construction and intelligent question answering, effectively promoting the development of geographic information towards intelligent services. |
| format | Article |
| id | doaj-art-a7a982ea699a46c18877bf138f114bdb |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-a7a982ea699a46c18877bf138f114bdb2025-08-20T02:12:38ZengMDPI AGApplied Sciences2076-34172025-01-01153107310.3390/app15031073A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural LanguageWenjuan Lu0Dongping Ming1Xi Mao2Jizhou Wang3Zhanjie Zhao4Yao Cheng5School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences Beijing, Beijing 100083, ChinaChinese Academy of Surveying and Mapping, Beijing 100036, ChinaChinese Academy of Surveying and Mapping, Beijing 100036, ChinaChinese Academy of Surveying and Mapping, Beijing 100036, ChinaChinese Academy of Surveying and Mapping, Beijing 100036, ChinaTo address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Decoding-enhanced BERT with disentangled attention)-based natural language spatiotemporal question semantic conversion model. The model first performs semantic encoding on natural language spatiotemporal questions, extracts pre-trained features based on the DeBERTa model, inputs feature vector sequences into BiGRU (Bidirectional Gated Recurrent Unit) to learn text features, and finally obtains globally optimal label sequences through a CRF (Conditional Random Field) layer. Then, based on the encoding results, it performs classification and semantic parsing of spatiotemporal questions to achieve question intent recognition and conversion to Cypher query language. The experimental results show that the proposed DeBERTa-based conversion model NL2Cypher can accurately achieve semantic information extraction and intent understanding in both simple and compound queries when using Chinese corpus, reaching an F1 score of 92.69%, with significant accuracy improvement compared to other models. The conversion accuracy from spatiotemporal questions to query language reaches 88% on the training set and 92% on the test set. The proposed model can quickly and accurately query spatiotemporal data using natural language questions. The research results provide new tools and perspectives for subsequent knowledge graph construction and intelligent question answering, effectively promoting the development of geographic information towards intelligent services.https://www.mdpi.com/2076-3417/15/3/1073semantic encodingnatural language spatiotemporal questionssemantic understandingDeBERTa |
| spellingShingle | Wenjuan Lu Dongping Ming Xi Mao Jizhou Wang Zhanjie Zhao Yao Cheng A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language Applied Sciences semantic encoding natural language spatiotemporal questions semantic understanding DeBERTa |
| title | A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language |
| title_full | A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language |
| title_fullStr | A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language |
| title_full_unstemmed | A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language |
| title_short | A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language |
| title_sort | deberta based semantic conversion model for spatiotemporal questions in natural language |
| topic | semantic encoding natural language spatiotemporal questions semantic understanding DeBERTa |
| url | https://www.mdpi.com/2076-3417/15/3/1073 |
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