A Survey of Deep Learning Methods for Relation Extraction

Abstract

Relation Extraction is an important sub-task of Information Extraction which has the potential of employing deep learning (DL) models with the creation of large datasets using distant supervision. In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.

References in corpus (4)

• Language Modeling with Gated Convolutional Networks
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• Deep Neural Network Based Relation Extraction: An Overview
• Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
• Literature review on vulnerability detection using NLP technology
• SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues
• Experiments on transfer learning architectures for biomedical relation extraction
• Relation Extraction using Explicit Context Conditioning
• Neural relation extraction: a survey
• Unique Chinese Linguistic Phenomena