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Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
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In this paper we describe an end to end Neural Model for Named Entity Recognition NER) which is based on Bi-Directional RNN-LSTM. Almost all NER systems for Hindi use Language Specific features and handcrafted rules with gazetteers. Our model is language independent and uses no domain specific features or any handcrafted rules. Our models rely on semantic information in the form of word vectors which are learnt by an unsupervised learning algorithm on an unannotated corpus. Our model attained state of the art performance in both English and Hindi without the use of any morphological analysis or without using gazetteers of any sort.
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Cited by 1 Pith paper
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Named Entity Recognition for Nepali Language
The authors claim the first neural NER for Nepali with a grapheme-level BiLSTM-CNN reaching 86.7 F1, but the evaluation uses a simplified IO tag scheme and the stated gains do not match the reported tables.
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