Word Sense Disambiguation: Supervised Program Interpretation Methodology for Punjabi Language

摘要

Word Sense Disambiguation (WSD) is the capability of finding the right interpretation of the given word in the given context through computation. Punjabi is among one of the 10 most widely spoken languages which is also morphologically rich but surprisingly, not much work has been done for computerization and development of lexical resources of this language. It is therefore motivating to develop a corpus of Punjabi language that will convey the correct sense of an ambiguous word. The availability of sense tagged corpora largely contributes in WSD and some of the most accurate WSD systems use supervised learning algorithms (like Naïve Bayes, k-NN and Decision Trees classifiers) to learn contextual rules or classification models automatically from sense-annotated examples. These algorithms have shown high accuracy in WSD and we are discussing these three supervised techniques, their algorithm, implementation and result when applied on Punjabi Corpora.