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We introduce globally normalized convolutional neural networks for jointentity classification and relation extraction. In particular, we propose a wayto utilize a linear-chain conditional random field output layer for predictingentity types and relations between entities at the same time. Our experimentsshow that global normalization outperforms a locally normalized softmax layeron a benchmark dataset.

Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification