Project: Part of Speech Tagging with Hidden Markov Models

Part of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often used to help disambiguate natural language phrases because it can be done quickly with high accuracy. Tagging can be used for many NLP tasks like determining correct pronunciation during speech synthesis (for example, dis-count as a noun vs dis-count as a verb), for information retrieval, and for word sense disambiguation.

In this notebook, the Pomegranate library is used to build a hidden Markov model for part of speech tagging using a "universal" tagset.