Improvement of Translation Accuracy for the Word Sense Disambiguation System using Novel Classifier Approach

Abstract

Machine Translation (MT) is a crucial application of Natural language Processing (NLP). This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is Machine Learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results. Text classification is one of the most significant techniques for resolving the WSD. In this paper, we have analyzed some common supervised ML text classification algorithms and also proposed a “hybrid model” called “AmbiF.” We have compared the results of all analyzed algorithms with the proposed model “AmbiF. The analyzed supervised algorithms are Decision Tree (DT), Bayesian network, Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The range of accuracy for all the algorithms that were examined is between sixty-eight and eighty-four percent. To improve the accuracy of the AmbiF model, we have merged the DT, SVM, and Naïve Bayes (NB)-classifier approach. For testing the model, we have used the ten-fold cross-validation test method. The AmbiF model’s accuracy has been reported eighty-five percent. Comparing the AmbiF model to all other analyzed supervised ML classification algorithms, it has also demonstrated great precision, recall, and F-score. Waikato Environment for Knowledge Analysis (WEKA)’s ML-tool is used to analyze the algorithms and the AmbiF model.