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Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity (NE) classes and is nowadays considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction,
question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes,
defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL). An appropriate tag conversion routine has been developed in order to convert the data into the forms tagged with the four NE tags, namely Person name, Location name, Organization
name and Miscellaneous name. The system makes use of the different contextual information of the words along with the variety of orthographic word-level features that are helpful in predicting the different NE classes. The system has been tested with the gold standard test sets
of 35K, and 38K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the overall recall, precision, and f-score values of 85.11%, 81.74%, and 83.39%, respectively, for Bengali and 82.76%, 77.81%, and 80.21%, respectively, for Hindi. Statistical analysis, ANOVA
is performed to show that the improvement in the performance with the use of language dependent features is statistically significant over the language independent features for Bengali and Hindi both.