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

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 (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, English… etc. This paper investigates the attention-based LSTM networks and simple Recurrent Neural Network in TIFINAGH part-of-speech (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 long short-term memory (LSTM) networks perform better than RNN, CRF and Decision Tree that has a near performance.