A Twitter Corpus for Named Entity Recognition in Turkish

Buse Çarık, Reyyan Yeniterzi


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
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4546–4551
Language:
URL:
https://aclanthology.org/2022.lrec-1.484
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2022.lrec-1.484.pdf
Code
 su-nlp/sunlp-twitter-ner-dataset