Arabic word sense disambiguation: a review

2018 ◽  
Vol 52 (4) ◽  
pp. 2475-2532 ◽  
Author(s):  
Bilel Elayeb
2016 ◽  
Vol 12 (1) ◽  
pp. 61-66 ◽  
Author(s):  
Marwah Alian ◽  
◽  
Arafat Awajan Awajan ◽  
Akram Al-Kouz Al-Kouz

Author(s):  
Marwah Alian ◽  
Arafat Awajan

The process of selecting the appropriate meaning of an ambigous word according to its context is known as word sense disambiguation. In this research, we generate a number of Arabic sense inventories based on an unsupervised approach and different pre-trained embeddings, such as Aravec, Fast text, and Arabic-News embeddings. The resulted inventories from the pre-trained embeddings are evaluated to investigate their efficiency in Arabic word sense disambiguation and sentence similarity. The sense inventories are generated using an unsupervised approach that is based on a graph-based word sense induction algorithm. Results show that the Aravec-Twitter inventory achieves the best accuracy of 0.47 for 50 neighbors and a close accuracy to the Fast text inventory for 200 neighbors while it provides similar accuracy to the Arabic-News inventory for 100neighbors. The experiment of replacing ambiguous words with their sense vectors is tested for sentence similarity using all sense inventories and the results show that using Aravec-Twitter sense inventory provides a better correlation value


2012 ◽  
Vol 24 (02) ◽  
pp. 133-151 ◽  
Author(s):  
ANIS ZOUAGHI ◽  
LAROUSSI MERHBÈNE ◽  
MOUNIR ZRIGUI

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