Machine learning based biomedical named entity recognition

The biomedical society makes wide use of text mining technology. Named Entity (NE) extraction is one of the most primary and significant tasks in biomedical information extraction of text mining technology. Named Entity Recognition (NER) involves processing structured and unstructured documents to recognize the definite kinds of entities and categorization of them into some predefined classes. Several Named Entity Recognition systems have been developed for the Biomedical Domain based on the Rule-Based, Dictionary based and Machine Learning based techniques. Implementing the best approach is not possible in all domains. Machine learning based approaches have many advantages than other approaches. In this paper we are proposing a Machine learning based framework for recognizing named entities from biomedical abstracts. For this study we used benchmarked datasets such as GENETAG and JNLPBA.