A Machine Learning Approach to POS Tagging Case study: Amazighe language

The development of automatic processing tools for amazighe language is hampered by the lack of resources for these. In this sense, one of the main objectives of the work reported in this article is to provide this language with a morphosyntactic annotated corpus and a better precision system for morphosyntaxic labeling. To do this, we started by building our corpus of over 60,000 words. This was first used to carry out the lexical segmentation step. Secondly, this corpus made it possible to train the different models of machine learning and deep learning; in order to develop a part of speech (POS) tagger of amazighe language.