Experiments with crowdsourced re-annotation of a POS tagging data set

TY - GEN

T1 - Experiments with crowdsourced re-annotation of a POS tagging data set

AU - Hovy, Dirk

AU - Plank, Barbara

AU - Søgaard, Anders

PY - 2014/6

Y1 - 2014/6

N2 - Crowdsourcing lets us collect multiple annotations for an item from several annotators. Typically, these are annotations for non-sequential classification tasks. While there has been some work on crowdsourcing named entity annotations, researchers have largely assumed that syntactic tasks such as part-of-speech (POS) tagging cannot be crowdsourced. This paper shows that workers can actually annotate sequential data almost as well as experts. Further, we show that the models learned from crowdsourced annotations fare as well as the models learned from expert annotations in downstream tasks.

AB - Crowdsourcing lets us collect multiple annotations for an item from several annotators. Typically, these are annotations for non-sequential classification tasks. While there has been some work on crowdsourcing named entity annotations, researchers have largely assumed that syntactic tasks such as part-of-speech (POS) tagging cannot be crowdsourced. This paper shows that workers can actually annotate sequential data almost as well as experts. Further, we show that the models learned from crowdsourced annotations fare as well as the models learned from expert annotations in downstream tasks.

M3 - Article in proceedings

SP - 377

EP - 382

BT - Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

PB - Association for Computational Linguistics

CY - Baltimore, Maryland

ER -