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Deep Neural Approaches to Relation Triplets Extraction: a Comprehensive Survey

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Abstract

The task of relation extraction is about identifying entities and relations among them in free text for the enrichment of structured knowledge bases (KBs). In this paper, we present a comprehensive survey of this important research topic in natural language processing. Recently, with the advances made in the continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction. To help future research, we present a comprehensive review of the recently published research works in relation extraction. Previous surveys on this task covered only one aspect of relation extraction that is pipeline-based relation extraction approaches at the sentence level. 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. We survey more than 100 publications in the field of relation extraction and present them in a structured way based on their similarity in the specific task they tried to solve, their model architecture, the datasets they used for experiments. We include the current state-of-the-art performance in several datasets in this paper for comparison. In this paper, we have covered different aspects of research in relation extraction field with a key focus on recent deep neural network-based methods. Also, we identify possible future research directions. Hopefully, this will help future researchers to identify the current research gaps and take the field forward.

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Notes

  1. https://github.com/percyliang/brown-cluster

  2. https://competitions.codalab.org/competitions/20717

  3. https://thunlp.github.io/2/fewrel2_nota.html

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  1. IIT Kharagpur, Kharagpur, India

    Tapas Nayak & Pawan Goyal

  2. Singapore University of Technology and Design, Singapore, Singapore

    Navonil Majumder & Soujanya Poria

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Nayak, T., Majumder, N., Goyal, P. et al. Deep Neural Approaches to Relation Triplets Extraction: a Comprehensive Survey. Cogn Comput 13, 1215–1232 (2021). https://doi.org/10.1007/s12559-021-09917-7

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