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

TL;DR

This paper presents a globally normalized CNN approach with a CRF layer for joint entity and relation classification, demonstrating improved performance over local normalization methods on benchmark datasets.

Contribution

The paper introduces a novel globally normalized CNN architecture with a CRF layer for simultaneous entity and relation classification, enhancing prediction accuracy.

Findings

Global normalization outperforms local softmax in accuracy.

CRF layer improves joint entity and relation prediction.

Method achieves state-of-the-art results on benchmark datasets.

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.