Natural Language Processing Based on Name Entity With N-Gram Classifier Machine Learning Process Through GE-Based Hidden Markov Model

Abstract

Natural Language Processing (NLP) is the computational linguistics mode for the identification and classification of text documents. The NLP process comprises the retrieval of the information for the classification of the sub-fields in the group based on the text document entities. With the higher morphological characters, it is challenging for the identify the entities in the languages. Recently, the machine learning model is deployed to evaluate the training data quality for the processing of text information data. This paper Hidden Markov Model (HMM) integrated with the name entity model for the categorization of the variables in the network. The HMM model uses the probability state such as start, transition and emission probability information for the computation of the sub-domain in the world files. The HMM model uses the n-gram classifier for the categorization for the sub-domain variables. The HMMNE model computes the ranking probability state of the work entities for the categorization of the variables in the network. The proposed HMMNE model achieves the higher precision value for the location with 99% and for the name and organization the precision state is achieved as the 98%. The examination of the developed HMMNE model is effective for the classification and identification of the variables in the name file entities variables.