Word2vec’s Distributed Word Representation for Hindi Word Sense Disambiguation

Author(s):  
Archana Kumari ◽  
D. K. Lobiyal
2020 ◽  
Vol 34 (10) ◽  
pp. 13947-13948
Author(s):  
Jie Wang ◽  
Zhenxin Fu ◽  
Moxin Li ◽  
Haisong Zhang ◽  
Dongyan Zhao ◽  
...  

Unsupervised WSD methods do not rely on annotated training datasets and can use WordNet. Since each ambiguous word in the WSD task exists in WordNet and each sense of the word has a gloss, we propose SGM and MGM to learn sense representations for words in WordNet using the glosses. In the WSD task, we calculate the similarity between each sense of the ambiguous word and its context to select the sense with the highest similarity. We evaluate our method on several benchmark WSD datasets and achieve better performance than the state-of-the-art unsupervised WSD systems.


2012 ◽  
Vol 23 (4) ◽  
pp. 776-785 ◽  
Author(s):  
Zhi-Zhuo YANG ◽  
He-Yan HUANG

Author(s):  
Manuel Ladron de Guevara ◽  
Christopher George ◽  
Akshat Gupta ◽  
Daragh Byrne ◽  
Ramesh Krishnamurti

2017 ◽  
Vol 132 ◽  
pp. 47-61 ◽  
Author(s):  
Yoan Gutiérrez ◽  
Sonia Vázquez ◽  
Andrés Montoyo

2005 ◽  
Vol 12 (5) ◽  
pp. 554-565 ◽  
Author(s):  
Martijn J. Schuemie ◽  
Jan A. Kors ◽  
Barend Mons

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