Natural Language Processing

4 credits

Introduction to classical methods that are useful in analyzing and mining real-world text data, including tokenization, stemming and lemmatization, bag-of-words classification, word embedding, language models, sentiment analysis, part-of-speech tagging, named entity recognition, and sequence-to-sequence models. Consideration of possible biases, privacy, and societal implications in these models. This is a distance education course. Prerequisites: DSC 255R. Restricted to major codes DS78 and DS79. All other students with graduate standing may be considered as space permits.