Research Article
Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning
Algorithm 1
Training procedure for
-Learning.
Initialize BILSTM, the Attention Layer, and Tree-LSTM with random parameters | | Pre-train BILSTM, the Attention Layer, and Tree-LSTM respectively | for epoch = do | for each input sentence do | Use the deep learning models above to automatically extract features of , and generate and . | for = 1, 2 do | = the reward and state after taking the action | | Perform gradient descent step: | | | The update rule is | | Where is update step, and is the reward function (Section 3.1), and is the state-action pair of next time. | | , | end for | end for | end for |
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