Comparative Analysis between Naïve Bayes Algorithm and Decision Tree to Solve WSD Using Empirical Approach
Boshra F. Zopon Al-Bayaty, Shashank D. Joshi · Lecture Notes on Software Engineering · 2015
There are many robust approaches algorithms for Word Sense Disambiguation using machine learning, and it is very difficult to make comparisons between them if we don't implementation empirically.In this word, analysis and developed JAVA Code and compare between two of the most successfully approaches for supervised machine learning, namely, Naï ve Bayes and Decision tree using WordNet and Senseval3 for Word Sense Disambiguation of words in context.While comparing these two approaches, paper deals with supervised learning method proving effective results.These algorithms refer common data set, training file and testing file to calculate accuracy to predict exact meaning of sense.