Creation of a NER-annotated corpus using Wikipedia and WikiData.
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Updated
Mar 20, 2024 - Python
Creation of a NER-annotated corpus using Wikipedia and WikiData.
A template for neural relation extraction
Workflow source code for ISPRAS-2021 journal paper "Language Models Application in Sentiment Attitude Extraction Task" (in Russian)
Linked Data Knowledge Base Population (KBP) framework built on top of Snorkel. The default configuration uses Wikipedia as text corpus and DBpedia as target.
Repository for the experiments and dataset described in "Simple Queries as Distant Labels for Predicting Gender on Twitter" presented at W-NUT 2017.
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
Resources for the paper "PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction"
NAACL 2019 "Structured Minimally Supervised Learning for Neural Relation Extraction"
A hybrid approach toward biomedical relation extraction training corpora: combining distant supervision with crowdsourcing
Codebase for the ACL 2023 paper: "Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data"
Dataset as a part of RANLP'2019 paper "Distant Supervision for Sentiment Attitude 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'
Implementation of Neural Relation Extraction with Selective Attention over Instances.
Code for AICS paper: "Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction"
Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings
Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction
MIL-RBERT: A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction (BioNLP @ ACL 2020)
Combining Distant and Direct Supervision for Neural Relation Extraction
[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
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