Resolving Ambiguities in Named Entity Recognition Using Machine Learning

In this paper, a named entity recognition model is proposed using data from Wikipedia. In every natural language, noun plays an important role. Named entity recognition is the process of identifying and tagging the proper noun in a text and then categorizing them on basis of names, location, product, and others. It has been performed in various languages using different approaches like rule-based, supervised or unsupervised learning. This paper presents a supervised learning algorithm which is used to train the classifier. Different combination rules are applied to the data to increase the performance of the model. Naive Bayes algorithm is also used to calculate the probability of different classes. The aim of this paper is to put forward a distinct approach and using these features analyze the performance measure of the system.