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

Sean Jones

Print-Friendly Page (opens a new window)