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

Heike Adel; Hinrich Schütze

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arXiv · 1707.07719

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

Heike Adel·Hinrich Schütze

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Abstract

We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.

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Heike Adel, Hinrich Schütze. 2017-07-24. Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification. https://arxiv.org/abs/1707.07719

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