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- W4385376543 abstract "The basic tasks of information extraction in biomedical text include named entity recognition and relation extraction. However, biomedical texts always contain a large number of sentences with complex structures, such as entity nesting and relation overlapping, which pose significant challenges to existing joint models. To address these issues, we propose a three-stage joint extraction model called BIJE, which uses an entity matrix-based strategy called BioPointer to solve the entity nesting problem, and the entity-relation exhaustive algorithm to tackle relation overlapping problem. Experimental results demonstrate that BIJE outperforms other methods on multiple biomedical datasets and prove that BIJE is a reliable framework for the information extraction from biomedical texts." @default.
- W4385376543 created "2023-07-30" @default.
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- W4385376543 date "2023-01-01" @default.
- W4385376543 modified "2023-09-25" @default.
- W4385376543 title "BIJE: A Joint Extraction Model for Biomedical Information Extraction" @default.
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- W4385376543 doi "https://doi.org/10.1007/978-981-99-4749-2_11" @default.
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