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- W4297464573 abstract "Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency structure information through graph convolutional networks. Meanwhile, in order to remove irrelevant information from the dependencies, the model adopts a new pruning strategy. Finally, the model adds a multi-head attention mechanism to focus on the entity information in the sentence from multiple aspects. We evaluate the proposed model on a Chinese medical entity relation extraction dataset. Experimental results show that our model can learn dependency relation information better and has higher performance than other baseline models." @default.
- W4297464573 created "2022-09-29" @default.
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- W4297464573 date "2022-09-28" @default.
- W4297464573 modified "2023-10-18" @default.
- W4297464573 title "Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information" @default.
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- W4297464573 doi "https://doi.org/10.3390/app12199781" @default.
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