Open AccessSupervised Word Sense Disambiguation using Decision Tree
Author(s) -
Sunita Rawat,
Kavita Kalambe,
Gaurav Kawade,
Nilesh Korde
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3323.078219
Subject(s) - computer science , natural language processing , artificial intelligence , semeval , sentence , ambiguity , machine translation , classifier (uml) , word (group theory) , semantic role labeling , task (project management) , linguistics , philosophy , management , programming language , economics
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.