CS 2731 / ISSP 2230: Introduction to Natural Language Processing (Fall 2020)
Professor
Dr. Diane Litman
TA
Nhat Tran (nlt26 at pitt.edu)
When & Where TuTh 1:15-2:30, via Zoom (https://canvas.pitt.edu/courses/47042) or 630 William Pitt Union
Office Hours Litman: Th 2:30-3:30 via class Zoom, by advance appointment Tu 9-10
Tran: We 3:00-4:30pm and Fr 10:00-11:00am via https://pitt.zoom.us/j/8491488727
Description This course provides an introduction to the field of natural language processing - the creation of programs that can understand, generate, and learn languages used by humans. It will expose students to applications by means of computational techniques including dynamic programming, hidden markov models, probalistic grammars, and machine learning algorithms.
Prerequisites: CS 1501 (algorithms) OR consent of the instructor
Textbook: Speech and Language Processing (3rd edition online draft - free!)
Required Work (tentative!!!) Homeworks (36%): written and programming
Exams (30%): final
Project (30%): presentation and written report
Participation (4%): discussion boards, other activities
CourseMirror (2% Extra Credit): submitting reflections
Late Penalty: For assignments that may be accepted late, the penalty is 2.5% per day up to 5 days including Saturday, Sunday, and holidays. Assignments are due by 11:59pm.
Date/Topic/Activities (Synchronous)
Readings/Videos (Asynchronous, before class)

Other Links; Assignments

August 20, 25
Introduction
Ch 1
Video: ACL 2020 McKeown Keynote
Reading: NLP is chasing the wrong goal
CourseMIRROR: download app, submit reflections
Canvas discussion board
I. Words
August 25, 27
Text Normalization
Ch 2 (2.1-2.4) Unix for Poets, pages 1-19
regular-expressions.info
September 1, 3
Language Modeling with N-Grams
Ch 3 (3.1-3.4)
Before 9/1:
  • Videos: #12-14, links in Canvas module
  • Reading: The Social Impact of NLP
  • Before 9/3:
  • Videos: #15-17
  • HW1: assigned 9/3, due 9/22
    September 8, 10, 15, 17
    Part-of-Speech Tagging
    Ch 8 (8.1-8.4.6)
    Before 9/8:
  • J&M 8.1-8.3
  • Video: #56
    Before 9/10:
  • J&M 8.4
  • Video: HMM/Viterbi
    Before 9/13:
  • Reading: Case Study of AAE
  • Reading Discussion: due 9/14
    II. Syntax
    September 17, 22, 24
    Constituency Grammars and Parsing
    Ch 12 (through 12.5), 13
    Before 9/15:
  • Videos 58-60
    Before 9/17:
  • Videos 61-65
  • September 24, 29
    Statistical Constituency Parsing
    Ch 14 (14.1-14.5, 14.8)
    Before 9/22:
  • Reading: Case Study of Gender Bias
  • Reading Discussion: due 9/23
    HW2 (see Canvas): assigned 9/24, due 10/8
    III. Machine Learning
    September 29, October 1
    Naive Bayes Classification (and Sentiment)
    Ch 4 (through 4.8)
    Before 9/29:
  • Videos 24-28
    Before 10/1:
  • Videos 29-32
  • TBD
    Logistic Regression
    Ch 5 (5.1-5.2; concepts in 5.3-5.7)
    TBD
    Representation Learning (and Vector Semantics)
    Ch 6
    TBD
    Neural Nets (and Language Models)
    Ch 7
    IV. Semantics
    V. Discourse and Applications
    TBD Final Exam and Project Presentations