  Named Entity Recognition (NER) is a useful component in Natural Language
Processing (NLP) applications. It is used in various tasks such as Machine
Translation, Summarization, Information Retrieval, and Question-Answering
systems. The research on NER is centered around English and some other major
languages, whereas limited attention has been given to Indian languages. We
analyze the challenges and propose techniques that can be tailored for
Multilingual Named Entity Recognition for Indian Languages. We present a human
annotated named entity corpora of 40K sentences for 4 Indian languages from two
of the major Indian language families. Additionally,we present a multilingual
model fine-tuned on our dataset, which achieves an F1 score of 0.80 on our
dataset on average. We achieve comparable performance on completely unseen
benchmark datasets for Indian languages which affirms the usability of our
model.
