Info 256

Applied Natural Language Processing

3 units

Course Description

This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems.


Proficient programming in Python (programs of at least 200 lines of code), proficient with basic statistics and probabilities

Requirements Satisfied

MIMS: Technology Requirement
Ph.D. Breadth — Engineering and Design
Ph.D. Major/Minor Areas — Information Organization and Retrieval
Ph.D. Major/Minor Areas — Information Systems Design
Applied Data Science Certificate — Elective

Last updated:

July 7, 2022