Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph
Abstract Identifying chemical-induced disease (CID) semantic relations in the biomedical literature, including both intra- and inter-sentence interactions, has significant implications for various downstream applications. Although various advanced methods have been proposed, they often overlook the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00305-7 |
| _version_ | 1827633230649491456 |
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| author | Quynh-Trang Pham Thi Quang Huy Dao Anh Duc Nguyen Thanh Hai Dang |
| author_facet | Quynh-Trang Pham Thi Quang Huy Dao Anh Duc Nguyen Thanh Hai Dang |
| author_sort | Quynh-Trang Pham Thi |
| collection | DOAJ |
| description | Abstract Identifying chemical-induced disease (CID) semantic relations in the biomedical literature, including both intra- and inter-sentence interactions, has significant implications for various downstream applications. Although various advanced methods have been proposed, they often overlook the cross-sentence dependency information, which is crucial for accurately predicting inter-sentence relations. In this study, we propose DEGREx, a novel graph-based neural model that presents a biomedical document as a dependency graph. DEGREx improves the long-distance relation extraction by allowing direct information exchange among document graph nodes through dependency connections. The information transition process is based on the idea of controller gates in long short-term memory networks. Our model, DEGREx, exerts a multi-task learning framework to jointly train relation extraction with named entity recognition, improving the performance of the CID extraction task. Experimental results on the benchmark dataset demonstrate that our model DEGREx outperforms all nine compared recent state-of-the-art models. |
| first_indexed | 2024-03-09T14:56:38Z |
| format | Article |
| id | doaj.art-eb360b99f37643e9bd55e5ce133d9207 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-09T14:56:38Z |
| publishDate | 2023-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-eb360b99f37643e9bd55e5ce133d92072023-11-26T14:11:56ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-08-0116111110.1007/s44196-023-00305-7Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency GraphQuynh-Trang Pham Thi0Quang Huy Dao1Anh Duc Nguyen2Thanh Hai Dang3Faculty of Information Technology, VNU University of Engineering and TechnologyFaculty of Information Technology, VNU University of Engineering and TechnologyFaculty of Information Technology, VNU University of Engineering and TechnologyFaculty of Information Technology, VNU University of Engineering and TechnologyAbstract Identifying chemical-induced disease (CID) semantic relations in the biomedical literature, including both intra- and inter-sentence interactions, has significant implications for various downstream applications. Although various advanced methods have been proposed, they often overlook the cross-sentence dependency information, which is crucial for accurately predicting inter-sentence relations. In this study, we propose DEGREx, a novel graph-based neural model that presents a biomedical document as a dependency graph. DEGREx improves the long-distance relation extraction by allowing direct information exchange among document graph nodes through dependency connections. The information transition process is based on the idea of controller gates in long short-term memory networks. Our model, DEGREx, exerts a multi-task learning framework to jointly train relation extraction with named entity recognition, improving the performance of the CID extraction task. Experimental results on the benchmark dataset demonstrate that our model DEGREx outperforms all nine compared recent state-of-the-art models.https://doi.org/10.1007/s44196-023-00305-7Chemical-induced disease extractionSemantic relation extractionDeep learningBiLSTM on dependency graph |
| spellingShingle | Quynh-Trang Pham Thi Quang Huy Dao Anh Duc Nguyen Thanh Hai Dang Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph International Journal of Computational Intelligence Systems Chemical-induced disease extraction Semantic relation extraction Deep learning BiLSTM on dependency graph |
| title | Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph |
| title_full | Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph |
| title_fullStr | Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph |
| title_full_unstemmed | Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph |
| title_short | Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph |
| title_sort | document level chemical induced disease semantic relation extraction using bidirectional long short term memory on dependency graph |
| topic | Chemical-induced disease extraction Semantic relation extraction Deep learning BiLSTM on dependency graph |
| url | https://doi.org/10.1007/s44196-023-00305-7 |
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