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

skweak: A software toolkit for weak supervision applied to NLP tasks

Learning Named Entity Tagger from Domain-Specific Dictionary

BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision

EMNLP 2018: RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information

Convolutional Neural Network for Multi-label Multi-instance Relation Extraction in Tensorflow

Code for NAACL 2019 paper: Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

[ACL 19] Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction

[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

Source code for paper "Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction" (EMNLP 2019)

Combining Distant and Direct Supervision for Neural Relation Extraction

ACL 2021

Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings

NAACL 2019 "Structured Minimally Supervised Learning for Neural Relation Extraction"

Korean Moview Review Emotion (KMRE) Dataset

MIL-RBERT: A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction (BioNLP @ ACL 2020)

Implementation of Neural Relation Extraction with Selective Attention over Instances.

Resources for the paper "PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction"

Earth observations, especially satellite data, have produced a wealth of methods and results in meeting global challenges, often presented in unstructured texts such as papers or reports. Accurate extraction of satellite and instrument entities from these unstructured texts can help to link and reuse Earth observation resources.