Relation Classification and Extraction
A related blog with interpretion of these papers HERE
A related code implementations of these papers HERE
NLP-progress in Relation Extraction
Relation Extraction : A Survey(2017)[paper]
DSRE Distant supervision for relation extraction without labeled data(2009) [paper]
CNN Convolution Neural Network for Relation Extraction(2013)[paper]
CNN Relation Classification via Convolutional Deep Neural Network(2014)[paper]
CR-CNN Classifying Relations by Ranking with Convolutional Neural Networks(2015)[paper]
RNN Relation Classification via Recurrent Neural Network(2015)[paper]
CNN Relation Extraction: Perspective from Convolutional Neural Networks(2015)[paper]
PCNN Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks(2015)[paper]
SATT Neural Relation Extraction with Selective Attention over Instances(2016)[paper]
MIMLCNN Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks(2016)[paper]
MLACNN Relation Classification via Multi-Level Attention CNNs(2016)[paper]
BILSTM Bidirectional Long Short-Term Memory Networks for Relation Classification(2016)[paper]
TRLSTM End-to-End Relation Extraction using LSTMs on Sequences and Tree Structure(2016)[paper]
ABLSTM Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification(2016)[paper]
HRNN+ATT Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention(2016) [paper]
CoType CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases(2016)[paper]
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning(2016)[paper]
BRCNN Bidirectional Recurrent Convolutional Neural Network for Relation Classification(2016) [paper]
APCNN Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions(2017)[paper]
CNN MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks(2017)[paper]
MNRE Neural Relation Extraction with Multi-lingual Attention(2017)[paper]
DRCNN Deep Residual Learning for Weakly-Supervised Relation Extraction(2017)[paper]
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix(2017)[paper]
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme(2017)[paper]
Joint Entity and Relation Extraction Based on A Hybrid Neural Network(2017)[paper]
Extracting Entities and Relations with Joint Minimum Risk Training(2018)[paper]
Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction(2018)[paper]
Joint Extraction of Entities and Relations Based on a Novel Graph Scheme(2018)[paper]
Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction(2018)[paper]
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention(2018)[paper]
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information(2018)[paper]
A Walk-based Model on Entity Graphs for Relation Extraction(2018)[paper]
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation(2018)[paper]
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing (2019)[paper]
Large Scaled Relation Extraction with Reinforcement Learning(2018)[paper]
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning(2018)[[paper]]
Reinforcement Learning for Relation Classification from Noisy Data(2018)[paper]