Authors:
Rakia Saidi
1
;
Fethi Jarray
2
and
Mohammed Alsuhaibani
3
Affiliations:
1
LIMTIC Laboratory, UTM University, Tunis, Tunisia
;
2
Higher Institute of Computer Science of Medenine, Gabes University, Medenine, Tunisia
;
3
Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia
Keyword(s):
Word Sense Disambiguation, Arabic text, Supervised Approach, Recurrent Neural Network.
Abstract:
Word Sense Disambiguation (WSD) refers to the process of discovering the correct sense of an ambiguous word occurring in a given context. In this paper, we address the problem of Word Sense Disambiguation of low-resource languages such as Arabic language. We model the problem as a supervised sequence-to-sequence learning where the input is a stream of tokens and the output is the sequence of senses for the ambiguous words. We propose four recurrent neural network (RNN) architectures including Vanilla RNN, LSTM, BiLSTM and GRU. We achieve, respectively, 85.22%, 88.54%, 90.77% and 92.83% accuracy with Vanilla RNN, LSTM, BiLSTM and GRU. The obtained results demonstrate superiority of GRU based deep learning Model for WSD over the existing RNN models.