Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
This survey helps researchers track the rapidly evolving field of relation extraction by organizing recent deep learning methods, datasets, and challenges.
This paper provides a comprehensive survey of deep neural network approaches for relation triplets extraction, covering recent advances from sentence-level to document-level methods, various neural architectures, and emerging research directions like zero-shot learning.
Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very difficult to keep track of so many papers. To help future research, we present a comprehensive review of the recently published research works in relation extraction. We mostly focus on relation extraction using deep neural networks which have achieved state-of-the-art performance on publicly available datasets. In this survey, we cover sentence-level relation extraction to document-level relation extraction, pipeline-based approaches to joint extraction approaches, annotated datasets to distantly supervised datasets along with few very recent research directions such as zero-shot or few-shot relation extraction, noise mitigation in distantly supervised datasets. Regarding neural architectures, we cover convolutional models, recurrent network models, attention network models, and graph convolutional models in this survey.