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- W2891935547 abstract "We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances." @default.
- W2891935547 created "2018-09-27" @default.
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- W2891935547 date "2018-01-01" @default.
- W2891935547 modified "2023-09-26" @default.
- W2891935547 title "Extracting Entities and Relations with Joint Minimum Risk Training" @default.
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- W2891935547 doi "https://doi.org/10.18653/v1/d18-1249" @default.
- W2891935547 hasPublicationYear "2018" @default.
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