Summary of the paper
| Title | Crowdsourcing and Annotating NER for Twitter #drift |
|---|---|
| Authors | Hege Fromreide, Dirk Hovy and anders Søgaard |
| Abstract | "We present two new NER datasets for Twitter; a manually annotated set of 1,467 tweets (kappa=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." |
| Topics | Social Media Processing, Crowdsourcing |
Full paper  |
Crowdsourcing and Annotating NER for Twitter #drift |
| Bibtex | @InProceedings{FROMREIDE14.421, author = {Hege Fromreide and Dirk Hovy and anders Søgaard}, title = {Crowdsourcing and Annotating NER for Twitter #drift}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |