Modeling sequential annotations for sequence labeling with crowds

@article{Lu2021ModelingSA,
  title={Modeling sequential annotations for sequence labeling with crowds},
  author={Xiaolei Lu and Tommy W. S. Chow},
  journal={IEEE transactions on cybernetics},
  year={2021},
  volume={PP}
}
  • Xiaolei LuT. Chow
  • Published 19 October 2021
  • Computer Science
  • IEEE transactions on cybernetics
Crowd sequential annotations can be an efficient and cost-effective way to build large datasets for sequence labeling. Different from tagging independent instances, for crowd sequential annotations, the quality of label sequence relies on the expertise level of annotators in capturing internal dependencies for each token in the sequence. In this article, we propose modeling sequential annotation for sequence labeling with crowds (SA-SLC). First, a conditional probabilistic model is developed to… 
1 Citations

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