Papers with CHMM-ALT
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing NER models are supervised by a large number of training sequences, each pre-annotated with token-level labels. |
| Approach: | They propose a conditional hidden Markov model which can effectively infer true labels from multi-source noisy labels in an unsupervised way. |
| Outcome: | The proposed model outperforms state-of-the-art weakly supervised NER models on four benchmarks from various domains. |