A Survey on Various Approaches Used in Named Entity Recognition for Indian Languages

Abstract

Named Entity Recognition (NER)” is a application of Artificial Intelligence , Machine Learning and “Natural Language Processing (NLP”).In NER, various classes of Named entity such as name of a person , an organization name, name of location, name of designation etc., are find out which is required in many NLP activities like question-answering system ,machine translation, artificial intelligence, summarization of documents, academics, robotics, Bioinformatics etc. Mostly NER task was evident for foreign languages but for Indian constitutional languages, due to some challenges present for example scarcity of resources, ambiguity present in languages, morphologically rich behavior of languages etc. ,NER work has been done for few of languages. In our paper, we presented several challenges available in NER for Indian languages and compared them by measuring various standard evaluation metric values like precision, recall and F-measure. In future extension, we would develop a efficient system, which would be more accurate, and which will cater many more Named Entity tags than existing systems ,for Indian languages NER tools.