Amazigh part-of-speech tagging with machine learning and deep learning

Otman Maarouf (Sultan Moulay Slimane University)
Rachid El Ayachi (Sultan Moulay Slimane University)
Mohamed Biniz (Sultan Moulay Slimane University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 24, No 3: December 2021

Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, and changes common dialects with computers in composed and spoken settings. At that point in scripts. Grammatical features part-of-speech (POS) allow marking the word as per its statement. We find in the literature that POS is used in a few dialects, in particular: French and English. This paper investigates the attention-based long short-term memory (LSTM) networks and simple recurrent neural network (RNN) in Tifinagh POS tagging when it is compared to conditional random fields (CRF) and decision tree. The attractiveness of LSTM networks is their strength in modeling long-distance dependencies. The experiment results show that LSTM networks perform better than RNN, CRF and decision tree that has a near performance.