Weakly-Supervised Named Entity Recognition
5 papers with code • 9 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Weakly-Supervised Named Entity Recognition
CoNLL 2003
BC5CDR
Most implemented papers
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
Yinghao-Li/CHMM-ALT
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ACL 2021
To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
cliang1453/BOND
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We study the open-domain named entity recognition (NER) problem under distant supervision.
Ontology-driven weak supervision for clinical entity classification in electronic health records
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules.
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels.
