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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Updated
Nov 23, 2019 - Python
Linked Data Knowledge Base Population (KBP) framework built on top of Snorkel. The default configuration uses Wikipedia as text corpus and DBpedia as target.
Dataset for paper "Weak Supervision for Fake News Detection via Reinforcement Learning" published in AAAI'2020.
Weakly supervised learning framework for classification.
In this project, we are using Snorkel Python to work with ML algorithms with an unlabeled text dataset.
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Framework to learn Named Entity Recognition models without labelled data using weak supervision.
PyTorch implementation of AAAI 2021 paper: A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
Implementation of 2D-3D Cyclic Generative Renderer (3DV-2020).
Weak Supervision Based Self Training for Few Shot Text Classification
BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision
Weak Labeling of Fake News Articles with Snorkel and Snuba
Weak Supervised Fake News Detection with RoBERTa, XLNet, ALBERT, XGBoost and Logistic Regression classifiers.
Data Programming by Demonstration (DPBD) for Document Classification
Includes additional materials for the following keras.io blog post.
Data programming by demonstration for information extraction and span annotation
PyTorch implementation of the model described my MS thesis: "Weakly Supervised Visual-Textual Grounding based on Concept Similarity" (https://github.com/lparolari/master-thesis)
Official data release to reproduce Confident Learning paper results
Data labeling using weak supervision
Utilizing the snorkel machine learning model to label biomimicry papers. Snorkel uses weak supervision to label large amounts of training data using programmatic labeling functions based on keyword rules.
The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.
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