  Named Entity Recognition is one of the most important text processing
requirement in many NLP tasks. In this paper we use a deep architecture to
accomplish the task of recognizing named entities in a given Hindi text
sentence. Bidirectional Long Short Term Memory (BiLSTM) based techniques have
been used for NER task in literature. In this paper, we first tune BiLSTM
low-resource scenario to work for Hindi NER and propose two enhancements namely
(a) de-noising auto-encoder (DAE) LSTM and (b) conditioning LSTM which show
improvement in NER task compared to the BiLSTM approach. We use pre-trained
word embedding to represent the words in the corpus, and the NER tags of the
words are as defined by the used annotated corpora. Experiments have been
performed to analyze the performance of different word embeddings and batch
sizes which is essential for training deep models.
