Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
arXiv:2103.16929 · doi:10.1007/s12559-021-09917-7
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.
A survey paper for relation extraction. Cogn Comput (2021)
References in corpus (16)
- Semi-Supervised Classification with Graph Convolutional Networks
- On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
- Cross-Sentence N-ary Relation Extraction with Graph LSTMs
- Fine-tune Bert for DocRED with Two-step Process
- Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
- Relation Extraction : A Survey
- A Survey of Deep Learning Methods for Relation Extraction
- End-to-end neural relation extraction using deep biaffine attention
- A Dependency-Based Neural Network for Relation Classification
- Contrastive Triple Extraction with Generative Transformer
- Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
- Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
- Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
- Leveraging Dependency Forest for Neural Medical Relation Extraction
- Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
- Deep Neural Networks for Relation Extraction