Supervised Word Sense Disambiguation using Decision Tree
Sunita Rawat
Sunita Rawat, Assitant Professor, Computer Science and Engineering Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Manuscript received on 04 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 4043-4047 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3323078219/19©BEIESP | DOI: 10.35940/ijrte.B3323.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Word sense disambiguation (WSD), Classification and regression tree (CART), polysemous word.
Scope of the Article: Classification