Named entity recognition by using maximum entropy

Named Entity Recognition (NER) is responsible for extracting and classifying some designators in the given specified text which can be name, location, organization etc. Since the last decade or so, researchers are greatly involved in this area as far as their interests are concerned. It is important procedure to extract the entities in a specified text based on a language which is termed as Natural Language. This language consists of various entities and the collection of such entities is called entity set. These entity sets are maintained in a uniform database called as gazetteer. In this paper we present a methodology called maximum entropy to retrieve the entity sets from the database. The machine is trained in such a way that it will retrieve the words which has the maximum entropy amongst all and has proved to be fastest method to extract and classify the entity sets from the database. The advantages of proposed method include sequence tagging which means this method has increased the freedom of choosing features to represent observations. © 2015 SERSC.