Abstract Machine learning based approach for Named Entity Recognition (NER) requires sufficient annotated corpus to train the classifier. Other NER resources like gazetteers are also required to make the classifier more accurate. But in many languages and domains relevant NER resources are still not available. Creation of adequate and relevant resources is costly and time consuming. However a large amount of resources and several NER systems are available in resource-rich languages, like English. Suitable language adaptation techniques, NER resources of a resource-rich language and minimally supervised learning might help to overcome such scenarios. In this paper we have studied a few such techniques in order to develop a Hindi NER system. Without using any Hindi NE annotated corpus we have achieved a reasonable accuracy of F-Measure 73.87 in the developed system.

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[52] Yamada H., Kudo T., and Matsumoto Y., Japanese Named Entity Extraction Using Support Vector Machine, Transactions of IPSJ, vol. 43, no. 1, pp. 44-53, 2002. Sujan Kumar Saha is an Assistant Professor in Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, India. His main research interests include Natural Language Processing, Machine Learning, and Educational Technologies. Mukta Majumder is an Assistant Professor in Department of Computer Science and Application, University of North Bengal, Siliguri, India. Prior to this he served Vidyasagar University as an Assistant Professor for almost three years. His main research interests include Text Processing, Machine Learning, Micro- fluidic System, and Biochip.