Applied Natural Language Processing
Applied Natural Language Processing
This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways. Topics include text-driven forecasting and prediction (using text for problems involving classification or regression); exploratory data analysis; experimental design; the representation of text, including features derived from linguistic structure (such as named entities, syntax, and coreference) and features derived from low-dimensional representations of words, sentences and documents; exploring textual similarity; information extraction (extracting relations between entities mentioned in text); and the underlying structure and affordances of large language models.