A Decision Tree Based Supervised Program Interpretation Technique for Gurmukhi Language

Deciphering the right context of the given word is one of the main challenges in Natural Language Processing. The study of Word Sense Disambiguation helps in deciphering the right context of the given word in use. Decision Tree is a methodology discussed under the supervised techniques used in WSD. Gurmukhi is one of the regional languages of India and much of the work done in this language is limited to knowledge-based mechanisms. The implementation of decision tree to correctly decipher the ambiguous word is new to this language and it has shown promising results with an average F-measure of 73.1%. These results will further help in Gurmukhi Word Sense Disambiguation.