Crowdsourcing and annotating NER for Twitter #drift

TY - GEN

T1 - Crowdsourcing and annotating NER for Twitter #drift

AU - Fromreide, Hege

AU - Hovy, Dirk

AU - Søgaard, Anders

PY - 2014

Y1 - 2014

N2 - We present two new NER datasets for Twitter; a manually annotated set of 1, 467 tweets (k = 0.942) and a set of 2, 975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in Finin et al. (2010). In our experiments with these datasets, we observe two important points: (a) language drift on Twitter is significant, and while off-the-shelf systems have been reported to perform well on in-sample data, they often perform poorly on new samples of tweets, (b) state-of-the-art performance across various datasets can be obtained from crowdsourced annotations, making it more feasible to "catch up" with language drift.

AB - We present two new NER datasets for Twitter; a manually annotated set of 1, 467 tweets (k = 0.942) and a set of 2, 975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in Finin et al. (2010). In our experiments with these datasets, we observe two important points: (a) language drift on Twitter is significant, and while off-the-shelf systems have been reported to perform well on in-sample data, they often perform poorly on new samples of tweets, (b) state-of-the-art performance across various datasets can be obtained from crowdsourced annotations, making it more feasible to "catch up" with language drift.

M3 - Article in proceedings

BT - Proceedings of the 9th International Conference on Language Resources and Evaluation

PB - European Language Resources Association

ER -