This is an introductory course in natural language processing (NLP). It explores a broad set of NLP tasks and introduces the students to the data, methods, and baseline solutions related to each. Topics covered include n-gram language models, text classification, part of speech tagging, word sense disambiguation, named entity extraction, information retrieval, and question answering. Methods explored include rule-based systems, classification with naïve bayes, sequence labeling with hidden Markov models and conditional random fields, as well as end-to-end systems. Offered by Info Sciences & Technology. May not be repeated for credit.
Introduction To Nlp
George Mason University
Python programming. Statistics or probability. Machine learning (desirable).