  Natural Language Processing (NLP) is a branch of artificial intelligence that
gives machines the ability to decode human languages. Partof-speech tagging
(POS tagging) is a pre-processing task that requires an annotated corpus.
Rule-based and stochastic methods showed remarkable results for POS tag
prediction. On this work, I performed a mathematical model based on Hidden
Markov structures and I obtained a high-level accuracy of ingredients extracted
from text recipe with performances greater than what traditional methods could
make without unknown words consideration.
