We study the problem of learning a named entity recognition (NER) model using noisy la-bels from multiple weak supervision sources. Though cheaper than human annotators, weak sources usually yield incomplete, inaccurate, or contradictorypredictions . To address such challenges , we propose a conditional hidden Markov model (CHMM) . It inherits the hidden Markov model's ability to aggregating the labels from weak sources through unsupervised learning. However, CHMM enhances the  hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens . In addition, we refine CHMM 's prediction with an alternate-training approach ( CHMM-AlT ). It fine-tunes a BERT-based NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return. Evaluation on four datasets from various domains shows that our method is superior to the weakly super-vised baselines by a wide margin .
We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model . 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. CHMM enhances the classic hidden Markov model with the contextual representation power of pre-trained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach ( CHMM-ALT ). It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return. Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins .
R
model using noisy la-bels
tagger using noisy labels
R
cheaper than human annotators, weak sources usually yield
cheap to obtain, the labels from weak supervision sources are often
R
or contradictorypredictions
and contradictory, making it difficult to learn an accurate NER model
R
such challenges
this challenge
R
. It inherits the hidden Markov model's ability to aggregating the labels from weak sources through unsupervised learning. However,
, which can effectively infer true labels from multi-source noisy labels in an unsupervised way.
A
classic
R
's flexibility and context representation capability by predicting
with the contextual representation power of pre-trained language models. Specifically, CHMM learns
R
. In addition, we refine CHMM 's prediction
to infer the latent true labels from noisy observations. We further refine CHMM
R
CHMM-AlT
CHMM-ALT
R
BERT-based NER
BERT-NER
R
Evaluation on four datasets
Experiments on four NER benchmarks
R
shows
show
R
is superior to the weakly super-vised baselines by a wide margin
outperforms state-of-the-art weakly supervised NER models by wide margins