Abstract
This paper introduces a new Turkish Twitter Named Entity Recognition dataset. The dataset, which consists of 5000 tweets from a year-long period, was labeled by multiple annotators with a high agreement score. The dataset is also diverse in terms of the named entity types as it contains not only person, organization, and location but also time, money, product, and tv-show categories. Our initial experiments with pretrained language models (like BertTurk) over this dataset returned F1 scores of around 80%. We share this dataset publicly.- Anthology ID:
- 2022.lrec-1.484
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4546–4551
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.484
- DOI:
- Cite (ACL):
- Buse Çarık and Reyyan Yeniterzi. 2022. A Twitter Corpus for Named Entity Recognition in Turkish. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4546–4551, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Twitter Corpus for Named Entity Recognition in Turkish (Çarık & Yeniterzi, LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2022.lrec-1.484.pdf
- Code
- su-nlp/sunlp-twitter-ner-dataset