Relation Extraction Based on Deep Learning

Abstract

Extracting entities and their relations is to detect entity mentions and recognize the semantic relation between them. Most of traditional methods are feature-based and treat this task as a pipeline of two separated tasks, i.e., named entity recognition and relation classification. Due to deep neural network models can learn effective entities and relations features from the given sentences without complicated manual feature engineering. Deep learning methods have been viewed as one of the major driving force in the recent development of natural language processing. We will briefly review the previous works on deep learning and give a brief overview of recent progresses on relation extraction. We implement the models based on deep neural networks both in pipeline methods and joint methods and come to conclusion about the comparison.