Abstract
We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines. It contains 1,338 news articles and more than 1.35 million tokens collected from Kawish and Awami Awaz Sindhi newspapers using the begin-inside-outside (BIO) tagging scheme. The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. The ultimate goal of developing SiNER is to present a gold-standard dataset for Sindhi NER along with quality baselines. We implement several baseline approaches of conditional random field (CRF) and recent popular state-of-the-art bi-directional long-short term memory (Bi-LSTM) models. The promising F1-score of 89.16 outputted by the Bi-LSTM-CRF model with character-level representations demonstrates the quality of our proposed SiNER dataset.- Anthology ID:
- 2020.lrec-1.361
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2953–2961
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.361
- DOI:
- Cite (ACL):
- Wazir Ali, Junyu Lu, and Zenglin Xu. 2020. SiNER: A Large Dataset for Sindhi Named Entity Recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2953–2961, Marseille, France. European Language Resources Association.
- Cite (Informal):
- SiNER: A Large Dataset for Sindhi Named Entity Recognition (Ali et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.lrec-1.361.pdf