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Decision Tree based Supervised Word Sense Disambiguation for Assamese

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Jumi Sarmah, Shikhar Kr. Sarma

Jumi Sarmah and Shikhar Kr. Sarma. Decision Tree based Supervised Word Sense Disambiguation for Assamese. International Journal of Computer Applications 141(1):42-48, May 2016. BibTeX

	author = {Jumi Sarmah and Shikhar Kr. Sarma},
	title = {Decision Tree based Supervised Word Sense Disambiguation for Assamese},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {1},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {42-48},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2016909488},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Word Sense Disambiguation (WSD) aims to disambiguate the words which have multiple sense in a context automatically. Sense denotes the meaning of a word and the words which have various meanings in a context are referred as ambiguous words. WSD is vital in many important Natural Language Processing tasks like MT, IR, TC, SP etc. This research paper attempts to propose a supervised Machine Learning approach- Decision Tree for Word Sense Disambiguation task in Assamese language. A Decision Tree is decision model flow-chart like tree structure where each internal node denotes a test, each branch represents result of a test and each leaf holds a sense label. J48 a Java implementation of C4.5 decision tree algorithm is taken for experimentation in our case. A few polysemous words with different real occurrences in Assamese text with manual sense annotation was collected as the training and test dataset. DT algorithm produces average F-measure of .611 when 10-fold crossvalidation evaluation was performed on 10 Assamese ambiguous words.


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Word Sense Disambiguation, Decision Tree, Assamese, Supervised approach