CSC 4641 - Natural Language Processing

3 lecture hours 0 lab hours 3 credits

Course Description
This course will cover various aspects of Natural Language Processing, including sentiment analysis, question answering, machine translation, and speech recognition. Students will construct some utilities from the ground up as well as use popular NLP libraries. (prereq: CSC 3310) (quarter system prereq: CS 3851)

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Apply regular expressions to tokenize and normalize text data
Use naive Bayes to construct and train basic language models using free-form text
Optimize language models using logistic regression
Understand the relationships between formal grammar and word meaning
Explain basic parsing techniques, including part-of-speech tagging, CNF grammar parsing, and dependency parsing
Use tools to construct a basic NLP system

Prerequisites by Topic

Significant programming experience
Algorithmic analysis
Data structures

Course Topics

Regular expressions
Normalization and tokenization
Edit distance
N-grams
Naive Bayes
Logistic regression
Vector semantics
Grammars and parsing
Information extraction
Sentiment analysis
Question answering
Machine translation
Phonetics and text-to-speech

Coordinator
Sean Jones

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