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Reseach Article

Decision Tree based Supervised Word Sense Disambiguation for Assamese

by Jumi Sarmah, Shikhar Kr. Sarma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 1
Year of Publication: 2016
Authors: Jumi Sarmah, Shikhar Kr. Sarma
10.5120/ijca2016909488

Jumi Sarmah, Shikhar Kr. Sarma . Decision Tree based Supervised Word Sense Disambiguation for Assamese. International Journal of Computer Applications. 141, 1 ( May 2016), 42-48. DOI=10.5120/ijca2016909488

@article{ 10.5120/ijca2016909488,
author = { Jumi Sarmah, 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 = {9},
url = { https://ijcaonline.org/archives/volume141/number1/24752-2016909488/ },
doi = { 10.5120/ijca2016909488 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:42:22.511505+05:30
%A Jumi Sarmah
%A Shikhar Kr. Sarma
%T Decision Tree based Supervised Word Sense Disambiguation for Assamese
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 1
%P 42-48
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Word Sense Disambiguation Decision Tree Assamese Supervised approach