Towards Deep Learning in Hindi NER: An approach to tackle the Labelled   Data Scarcity
  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. 
Vinayak Athavale, Shreenivas Bharadwaj, Monik Pamecha, Ameya Prabhu   and Manish Shrivastava
cs.CL cs.LG