Experiments with crowdsourced re-annotation of a POS tagging data set
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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 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.
Original language | English |
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Title of host publication | Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) |
Place of Publication | Baltimore, Maryland |
Publisher | Association for Computational Linguistics |
Publication date | Jun 2014 |
Pages | 377-382 |
Publication status | Published - Jun 2014 |
ID: 107673017