Decision Tree based Supervised Word Sense Disambiguation for Assamese
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10.5120/ijca2016909488 |
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
@article{10.5120/ijca2016909488,
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 = {http://www.ijcaonline.org/archives/volume141/number1/24752-2016909488},
doi = {10.5120/ijca2016909488},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, 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.
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Keywords
Word Sense Disambiguation, Decision Tree, Assamese, Supervised approach