A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
[article]
2021
arXiv
pre-print
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain
arXiv:2105.14980v1
fatcat:h62jkrs6wnalnlrf6mcatsd6d4