Weakly-Supervised Named Entity Recognition

5 papers with code • 9 benchmarks • 5 datasets

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Most implemented papers

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

Yinghao-Li/CHMM-ALT 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 28 Jun 2020

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

som-shahlab/trove 5 Aug 2020

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

zhaoxy92/GLaRA EACL 2021

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

Yinghao-Li/Sparse-CHMM 27 May 2022

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.