awesome-relation-extraction

Relation extraction resource

A curated collection of resources and papers on relation extraction, providing an overview of the state-of-the-art in NLP research.

📖 A curated list of awesome resources dedicated to Relation Extraction, one of the most important tasks in Natural Language Processing (NLP).

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NLP progress: Relationship Extraction
Named Entity Recognition and Relation Extraction:State-of-the-Art (Nasar et al., 2021)
A Survey of Deep Learning Methods for Relation Extraction (Kumar, 2017)
A Survey on Relation Extraction (Bach and Badaskar, 2017)
Relation Extraction: A Survey (Pawar et al., 2017)
A Review on Entity Relation Extraction (Zhang et al., 2017)
Review of Relation Extraction Methods: What is New Out There? (Konstantinova et al., 2014)
100 Best Github: Relation Extraction

Awesome Relation Extraction / Papers / Supervised Approaches
[paper] Convolution Neural Network for Relation Extraction
[paper] Relation Classification via Convolutional Deep Neural Network
[paper] Relation Extraction: Perspective from Convolutional Neural Networks
[paper] Classifying Relations by Ranking with Convolutional Neural Networks
[paper] Attention-Based Convolutional Neural Network for Semantic Relation Extraction
[paper] Relation Classification via Multi-Level Attention CNNs
[paper] MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
[paper] Relation Classification via Recurrent Neural Network
[paper] Bidirectional Long Short-Term Memory Networks for Relation Classification
[paper] End-to-End Relation Extraction using LSTMs on Sequences and Tree Structure
[paper] Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
[paper] Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention
[paper] Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
[paper] Semantic Compositionality through Recursive Matrix-Vector Spaces
[paper] Factor-based Compositional Embedding Models
[paper] A Dependency-Based Neural Network for Relation Classification
[paper] Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
[paper] Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
[paper] Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation
[paper] Bidirectional Recurrent Convolutional Neural Network for Relation Classification
[paper] Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
[paper] Matching the Blanks: Distributional Similarity for Relation Learning
[paper] Relation of the Relations: A New Paradigm of the Relation Extraction Problem
[paper] GDPNet: Refining Latent Multi-View Graph for Relation Extraction
[parer] RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

Awesome Relation Extraction / Papers / Distant Supervision Approaches
[paper] Distant supervision for relation extraction without labeled data
[paper] Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
[paper] Multi-instance Multi-label Learning for Relation Extraction
[paper] Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
[paper] Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks
[paper] Incorporating Relation Paths in Neural Relation Extraction
[paper] Neural Relation Extraction with Selective Attention over Instances
[paper] Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text
[paper] Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention
[paper] RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
[paper] Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions

Awesome Relation Extraction / Papers / Language Models
[paper] Enriching Pre-trained Language Model with Entity Information for Relation Classification
[paper] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
[paper] SpanBERT: Improving pre-training by representing and predicting spans
[paper] Efficient long-distance relation extraction with DG-SpanBERT
[paper] Improving Relation Extraction by Pretrained Language Representations

Awesome Relation Extraction / Papers / Knowledge Graph Based Approaches
[paper] KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

Awesome Relation Extraction / Papers / Few-Shot Learning Approaches
[paper] FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Awesome Relation Extraction / Papers / Miscellaneous
[paper] Jointly Extracting Relations with Class Ties via Effective Deep Ranking
[paper] End-to-End Neural Relation Extraction with Global Optimization
[paper] Adversarial Training for Relation Extraction
[paper] A neural joint model for entity and relation extraction from biomedical text
[paper] Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning
[paper] TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations

Awesome Relation Extraction / Datasets
[paper] SemEval-2010 Task 8
[paper] New York Times (NYT) Corpus
[paper] FewRel: Few-Shot Relation Classification Dataset
[paper] TACRED: The TAC Relation Extraction Dataset
[Website] ACE05:
[paper] SemEval-2018 Task 7

Awesome Relation Extraction / Videos and Lectures
Stanford University: CS124 , Dan Jurafsky

Awesome Relation Extraction / Videos and Lectures / Stanford University: CS124
Week 5: Relation Extraction and Question (Video)

Awesome Relation Extraction / Videos and Lectures
Washington University: CSE517 , Luke Zettlemoyer

Awesome Relation Extraction / Videos and Lectures / Washington University: CSE517
Relation Extraction 1 (Slide)
Relation Extraction 2 (Slide)

Awesome Relation Extraction / Videos and Lectures
New York University: CSCI-GA.2590 , Ralph Grishman

Awesome Relation Extraction / Videos and Lectures / New York University: CSCI-GA.2590
Relation Extraction: Rule-based Approaches (Slide)

Awesome Relation Extraction / Videos and Lectures
Michigan University: Coursera , Dragomir R. Radev

Awesome Relation Extraction / Videos and Lectures / Michigan University: Coursera
Lecture 48: Relation Extraction (Video)

Awesome Relation Extraction / Videos and Lectures
Virginia University: CS6501-NLP , Kai-Wei Chang

Awesome Relation Extraction / Videos and Lectures / Virginia University: CS6501-NLP
Lecture 24: Relation Extraction (Slide)

Awesome Relation Extraction / Systems
DeepDive
Stanford Relation Extractor

Awesome Relation Extraction / Frameworks
[github] 4,344 11 months ago
[github] 59 10 days ago

Awesome Relation Extraction / Frameworks / [github]
[paper] 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

Awesome Relation Extraction / Frameworks
[github] 11 almost 6 years ago