CS481: Intelligent Text Analysis and Knowledge Mining

Illinois Institute of Technology

Semester: Spring-2021

Instructor: Zhao Wang

Class schedule: 06:45 - 08:00 pm T/R Online

E-mail: zwang185 at hawk.iit.edu

Office Hours: 08:00 - 9:00 pm Thur

TA: Ruo Yang (ryang23 at hawk.iit.edu)

See the Syllabus for a detailed list of topics and schedules.

Prerequisites

• Math
  • Probability
    
  • Linear algebra
    
• Programming
  • Basic algorithms and data structure
    
  • Python 3
    
• Prior knowledge of Machine Learning, Text Processing, and Data Mining will be helpful

Course Objectives

• Practical introduction to NLP using real world examples and tasks.
• Learn basic linguistic concepts (e.g., Morphology, Syntax, Semantics, Pragmatics).
• Introduce methods that make language accessible to computers from different levels (e.g., Word, Phrase / Group, Sentence / Clause, Text / Discourse).
• Apply NLP techniques to a variaty of real world text analysis tasks (e.g., Part-Of-Speech Tagging, Text Classification, Named Entity Recognition, Information Extraction).

Textbooks

• Natural Language Processing with Python, Byrd et al.
• Natural Language Processing with PyTorch, Delip Rao and Brian McMahan.
• Foundations of Statistical Natural Language Processing, Chris Manning and Hinrich Schütze.
• Speech and Language Processing, Jurafsky & Martin
  • 1st edition
  • 3rd edition, which is in progress).

Related courses

• UMD
• UMass I
• UMass II
• UMass III
• GaTech
• Stanford I
• Stanford II
• Berkeley
• UTexas
• Columbia
• https://www.coursera.org/specializations/natural-language-processing
• https://github.com/ageron/handson-ml
• https://www.coursera.org/learn/neural-networks
• https://www.coursera.org/learn/machine-learning/

Grading

100 points - Quizzes (10 @ 10 points each)
100 points - Assignments (4 @ 25 points each)
100 points - Midterm
100 points - Final
100 points - Individual Project
500 total points

Percent	Grade
100-90	A
89-80	B
79-70	C
< 70	E

Academic Integrity

• Please read IIT's Academic Honesty Policy
• All work you turn in must be done by you alone
• You may not look at the solution of any other student prior to the due date.
• All violations will be reported to academichonesty@iit.edu.
• The first violation will result in a failing grade for that assignment/test. The second will result in a failing grade for the course.
• Cheating during the quizzes and tests will result in a failing grade for the course.

Late Submission Policy

• Late assignments will not be accepted, unless:
  • There is an unavoidable medical, family, or other emergency; and
  • You notify me prior to the due date.