skweak: A software toolkit for weak supervision applied to NLP tasks
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Updated
Sep 2, 2024 - Python
skweak: A software toolkit for weak supervision applied to NLP tasks
BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision
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
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"
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"
Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction
[EMNLP 2023] Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Code and dataset for IJCNN2019 paper 'Dilated Convolutional Networks Incorporating Soft Entity Type Constraints for Distant Supervised Relation Extraction'
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
Code for AICS paper: "Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction"
Codebase for the ACL 2023 paper: "Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data"
Linked Data Knowledge Base Population (KBP) framework built on top of Snorkel. The default configuration uses Wikipedia as text corpus and DBpedia as target.
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