Natural Language Processing (NLP)

Catalog description

In this course, students study the complexities of analyzing linguistic information both in text and voice forms. Students examine the complexities of language phenomena and how to handle those using current NLP tools and scripting techniques. The course covers topics and implementations such as tokenization, sentence structure, grammars, parsing, machine translation, and sentiment analysis. Students discuss and examine how data selection and sampling across genres affects NLP systems, including learning how to leverage social media data along with more formal language sources for English and multilingual data. Course covers current applications of NLP in artificial intelligence applications such as text-to-speech, language translation, language understanding, and language generation. Minimum passing grade of C. Prerequisite: DATA 325 with a grade of C or higher