Domain Specific Named Entity Recognition Using Supervised Approach

This paper introduces Named Entity Recognition approach for text corpus. Supervised Statistical methods are used to develop our system. Our system can be used to categorize NEs belonging to a particular domain for which it is being trained. As Named Entities appears in text surrounded by contexts (words that are left or right of the NE), we will be focusing on extracting NE contexts from text and then performing statistical computing on them. We are using n-gram model for extracting contexts from text. Our methodology first extracts left and right tri-grams surrounding NE instances in the training corpus and calculate their probabilities. Then all the extracted tri-grams along with their calculated probabilities are stored in a file. During testing, system detects unrecognized NEs from the testing corpus and categorizes them using the tri-gram probabilities calculated during training time. The proposed system is made up of two modules i.e. Knowledge acquisition and NE Recognition. Knowledge acquisition module extracts tri-grams surrounding NEs in the training corpus and NE Recognition module performs the categorization o