Weakly-Supervised Named Entity Recognition
5 papers with code • 9 benchmarks • 5 datasets
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.
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.
Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules.
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.