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The joint entity and relation extraction task aims to extract all relational triples from a sentence.
Non-Autoregressive Transformer Automatic Speech Recognition
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Kernel methods for relation extraction
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Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
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Modeling relations and their mentions without labeled text
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Exploiting syntactico-semantic structures for relation extraction
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Incremental joint extraction of entity mentions and relations
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Sequence to sequence learning with neural networks
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Relation Classification via Convolutional Deep Neural Network
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Classifying relations via long short term memory networks along shortest dependency paths
Xu, Y.; Mou, L.; Li, G.; Chen, Y.; Peng, H.; and Jin, Z. 2015 · 2015
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Table filling multi-task recurrent neural network for joint entity and relation extraction
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Extracting relational facts by an end-to-end neural model with copy mechanism
Zeng, X.; Zeng, D.; He, S.; Liu, K.; and Zhao, J. 2018 · 2018
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Joint extraction of entities and overlapping relations using position-attentive sequence labeling
Dai, D.; Xiao, X.; Lyu, Y.; Dou, S.; She, Q.; and Wang, H. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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GraphRel: Modeling text as relational graphs for joint entity and relation extraction
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FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Ma, X.; Zhou, C.; Li, X.; Neubig, G.; and Hovy, E. 2019 · 2019
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Attention is all you need
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End-to-end neural relation extraction with global optimization
Zhang, M.; Zhang, Y.; and Fu, G. 2017 · 2017
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Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
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Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction
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Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
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A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
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