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Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph

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Názov: Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph
Autori: Quynh-Trang Pham Thi, Quang Huy Dao, Anh Duc Nguyen, Thanh Hai Dang
Zdroj: International Journal of Computational Intelligence Systems, Vol 16, Iss 1, Pp 1-11 (2023)
Informácie o vydavateľovi: Springer, 2023.
Rok vydania: 2023
Zbierka: LCC:Electronic computers. Computer science
Predmety: Chemical-induced disease extraction, Semantic relation extraction, Deep learning, BiLSTM on dependency graph, Electronic computers. Computer science, QA75.5-76.95
Popis: 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.
Druh dokumentu: article
Popis súboru: electronic resource
Jazyk: English
ISSN: 1875-6883
Relation: https://doaj.org/toc/1875-6883
DOI: 10.1007/s44196-023-00305-7
Prístupová URL adresa: https://doaj.org/article/eb360b99f37643e9bd55e5ce133d9207
Prístupové číslo: edsdoj.b360b99f37643e9bd55e5ce133d9207
Databáza: Directory of Open Access Journals
Popis
Abstrakt: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.
ISSN:18756883
DOI:10.1007/s44196-023-00305-7