SiNER: A Large Dataset for Sindhi Named Entity Recognition
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.
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