Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
Heike Adel, Hinrich Schütze · 2017
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.