Kudo, T., Matsumoto, Y.: Chunking with support vector machine. Experimental results show that our approach achieves higher accuracy than previous approaches. We have used required lexical databases to prepare rules and identify the context patterns for Odia. Moreover, some gazetteers and context patterns are added to the system to increase its performance as it is observed that identification of rules and context patterns requires language-based knowledge to make the system work better. Some language specific rules are added to the system to recognize specific NE classes. Starting with named entity annotated corpora and a set of features it requires to develop a base-line NER System. NER aims at classifying each word in a document into predefined target named entity classes in a linear and non-linear fashion. The development of a NER system for Odia using Support Vector Machine is a challenging task in intelligent computing. ![]() ![]() This paper presents a novel approach to recognize named entities in Odia corpus.
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