Study Of Supervised Machine Learning Approaches For Word Sense Disambiguation Of Parts-Of-Speech Ambiguity

Abstract

One of the most important applications of Natural Language Processing is Machine Translation (MT). It is an automated process of translation through a computer system. Machine Learning (ML) is one of the recent methods used in MT, and it has become very popular in the area of research over the last numerous years. Ambiguity is a major challenge in MT. ML has given promising results in terms of system learning and predicting results. The text classification technique in Machine Learning is considered as one of the most important methods to resolve Word Sense Disambiguation (WSD). The role of Data set both as Training and Test data is important to predict the required results. We have also done an analysis on supervised machine learning text classification algorithms namely Naïve Bayes’, Decision Tree, Support Vector Machine (SVM), K-nearest Neighbor (KNN), Neural Network, Logistic Regression, and Random Forest