awesome-relation-extraction

(Nasar et al., 2021)

(Kumar, 2017)

(Bach and Badaskar, 2017)

(Pawar et al., 2017)

(Zhang et al., 2017)

(Konstantinova et al., 2014)

Convolution Neural Network for Relation Extraction

Relation Classification via Convolutional Deep Neural Network

Relation Extraction: Perspective from Convolutional Neural Networks

Classifying Relations by Ranking with Convolutional Neural Networks

Attention-Based Convolutional Neural Network for Semantic Relation Extraction

Relation Classification via Multi-Level Attention CNNs

MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks

Relation Classification via Recurrent Neural Network

Bidirectional Long Short-Term Memory Networks for Relation Classification

End-to-End Relation Extraction using LSTMs on Sequences and Tree Structure

Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention

Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

Semantic Compositionality through Recursive Matrix-Vector Spaces

Factor-based Compositional Embedding Models

A Dependency-Based Neural Network for Relation Classification

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation

Bidirectional Recurrent Convolutional Neural Network for Relation Classification

Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Matching the Blanks: Distributional Similarity for Relation Learning

Relation of the Relations: A New Paradigm of the Relation Extraction Problem

GDPNet: Refining Latent Multi-View Graph for Relation Extraction

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

Distant supervision for relation extraction without labeled data

Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

Multi-instance Multi-label Learning for Relation Extraction

Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks

Incorporating Relation Paths in Neural Relation Extraction

Neural Relation Extraction with Selective Attention over Instances

Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text

Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention

RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information

Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions

Enriching Pre-trained Language Model with Entity Information for Relation Classification

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

SpanBERT: Improving pre-training by representing and predicting spans

Efficient long-distance relation extraction with DG-SpanBERT

Improving Relation Extraction by Pretrained Language Representations

KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Jointly Extracting Relations with Class Ties via Effective Deep Ranking

End-to-End Neural Relation Extraction with Global Optimization

Adversarial Training for Relation Extraction

A neural joint model for entity and relation extraction from biomedical text

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations

SemEval-2010 Task 8

New York Times (NYT) Corpus

FewRel: Few-Shot Relation Classification Dataset

TACRED: The TAC Relation Extraction Dataset

ACE05:

SemEval-2018 Task 7

, Dan Jurafsky

(Video)

, Luke Zettlemoyer

(Slide)

(Slide)

, Ralph Grishman

(Slide)

, Dragomir R. Radev

(Video)

, Kai-Wei Chang

(Slide)

Is an open-source and extensible toolkit focused on data preparation for document-level relation extraction organization. It complements the OpenNRE functionality, as in terms of the latter, (2.4 ). The core functionality includes (1) API for document presentation with EL (Entity Linking, i.e. Object Synonymy) support for sentence level relations preparation (dubbed as contexts) (2) API for contexts extraction (3) relations transferring from sentence-level onto document-level, etc. It provides (like OpenNRE) and modules, both applicable for sentiment attitude extraction task