Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey

• URL: http://arxiv.org/abs/2103.16929v1
• Date: Wed, 31 Mar 2021 09:27:15 GMT
• Title: Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
• Authors: Tapas Nayak and Navonil Majumder and Pawan Goyal and Soujanya Poria
• Abstract summary: We focus on relation extraction using deep neural networks on publicly available datasets. We cover sentence-level relation extraction to document-level relation extraction, pipeline-based approaches to joint extraction approaches, annotated datasets to distantly supervised datasets. Regarding neural architectures, we cover convolutional models, recurrent network models, attention network models, and graph convolutional models in this survey.
• Score: 22.586079965178975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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