Supervised Word Sense Disambiguation using Decision Tree

Abstract

Semantic processing is an essential task in natural language processing. In semantic processing it has observed that some words have more than one meaning. Multiple meanings of a word create serious problems to linguists which produces ambiguity in sentence. Word Sense Disambiguation is one of the main challenges in natural language processing which is present in almost all the languages. By existing knowledge and experience human can certainly disambiguate the words but for machine it is difficult task. In the proposed work, we are resolving the ambiguity of all open class word in English sentence and translating it to the Hindi sentence. We have used decision tree as a classifier. For improving the speed of translation we have used the concept of translation memory.

A Literature Survey on Word Sense Disambiguation for the Hindi Language

“…A decision tree is a classification method that repeatedly divides the training dataset and organizes the classification rules in a tree-like structure [26,27]. Every interior node of the decision tree represents a test performed on an attribute value, and the branches represent the outcomes of the test.…”