Natalie Parde

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CS 421: Natural Language Processing

Fall 2022

Contact Information

Professor: Natalie Parde (
Office Hours: Tuesday 10:00 - 11:00 a.m. / Thursday 4:00 - 5:00 p.m. CST
Teaching Assistant: Usman Shahid
Office Hours: TBD

What is this class about?

Natural language processing (NLP) is the subfield of artificial intelligence that focuses on automatically understanding and generating natural language (e.g., Arabic, Navajo, Spanish, or English). It is crucial to many everyday applications ...if you've searched for something online or talked to one of your devices today, you've made use of many different NLP technologies already. This class will provide an introduction to the foundations and most popular applications of natural language processing, through a combination of readings, lectures, short assignments, and projects. Topics covered will include text preprocessing, part-of-speech tagging, syntactic and dependency parsing, language modeling, word representations, text classification, and dialogue systems, among others.


Readings, learning content, and (some) assignments for this class will be drawn from the following source:
- Daniel Jurafsky and James H Martin. Speech and Language Processing (3rd Edition). Draft, 2022.

This textbook is still being written; its current draft can be freely accessed at the link above.


This is a 400-level course, designed for both graduate students and advanced undergraduates. Depending on your classification, you may have enrolled in either the four-hour version (grad students) or the three-hour version (undergrad students). There are slightly different requirements for the two versions of the course, with the biggest difference being that grad students will be required to complete a semester-long reproducibility study. Undergrads may opt to complete a reproducibility study as well if they would like, in which case their final course grade will be determined according to the same breakdown as that used for graduate students; however, doing this extra work is certainly not a requirement. Some further details about the work you will be expected to complete for this course are provided below:
  • Python Bootcamp (Assignment 0): One introductory coding "bootcamp" assignment will be due before submitting the first standard deliverable, to ensure necessary Python proficiency.
  • Assignments: Four assignments will be due over the course of the semester (due dates are indicated on the course calendar). These assignments will contain a mix of theoretical and coding questions. Code should be written in Python.
  • Project: All students will complete a semester-long project, divided into four deliverables (due dates are indicated on the course calendar). Code, when applicable, should be written in Python.
  • Reproducibility Study: Graduate students (and any undergraduates who choose to do so) will complete a semester-long reproducibility study, due the week before finals week. For the reproducibility study, students will analyze the ease with and extent to which a selected NLP research paper's findings can be reproduced. Reproducibility studies can be completed individually or in pairs; if done in pairs, the submission must be accompanied by a statement detailing which component(s) each student worked on, signed by both students.

Grading rubrics will be posted in the deliverables' descriptions. Final course grades will be determined according to the following breakdowns:
  • Undergraduate Students:
    • Python Bootcamp (Assignment 0): 4%
    • Project: 56% (14% for each deliverable)
    • Assignments: 40% (10% for each assignment)
  • Graduate Students:
    • Python Bootcamp (Assignment 0): 4%
    • Project: 40% (10% for each deliverable)
    • Assignments: 40% (10% for each assignment)
    • Reproducibility Study: 16% (4% for the presentation, 8% for the report, and 4% for the source code)


Below is a list of course topics, readings, deadlines, and slides by week. The version of the schedule here is subject to change. All deliverables are due by 12:00 p.m. (noon) CST on the specified due date.

Week Topic Readings Deliverables Slides
8/22-8/26 Introduction and Dialogue Systems and Chatbots Chapter 24 Download
8/29-9/2 Text Preprocessing and Edit Distance Chapter 2 Download
9/5-9/9 Hidden Markov Models and Language Modeling with N-Grams Appendix A and Chapter 3 Assignment 0 (9/9; Recommended much sooner!)

Assignment 1 (9/9)
9/12-9/16 Naive Bayes and Text Classification Chapter 4 Download
9/19-9/23 Logistic Regression and Conditional Random Fields Chapter 5 Project Part 1 (9/23) Download
9/26-9/30 Vector Semantics and Embeddings Chapter 6 Assignment 2 (9/30) Download
10/3-10/7 Neural Networks and Neural Language Models Chapter 7 Download
10/10-10/14 Part-of-Speech Tagging and Constituency Grammars Chapters 8 and 12 Project Part 2 (10/14) Download
10/17-10/21 Constituency Parsing Chapters 13 and Appendix C Assignment 3 (10/21) Download
10/24-10/28 Dependency Parsing and Logical Representations of Sentence Meaning Chapters 14 and 15 Download
10/31-11/4 Word Senses and WordNet and Semantic Role Labeling Chapters 18 and 19 Project Part 3 (11/4) Download
11/7-11/11 Lexicons for Sentiment, Affect, and Connotation Chapter 20 Assignment 4 (11/11) Download
11/14-11/18 Coreference Resolution Chapter 21 Download
11/21-11/25 Discourse Coherence Chapter 22 Project Part 4 (11/23) Download
11/28-12/2 Wrap-Up and Reproducibility Presentations Videos (11/28) or Presentations (Tuesday/Thursday); Source Code and Report (12/2)

Final Notes

This website is provided partially for student convenience, partially for my own record-keeping purposes, and partially for the benefit of others who are not able to enroll in the course but who may find the content interesting for one reason or another. It is not a substitute for the course pages on Blackboard and Gradescope, or the course discussion board on Piazza! Please refer to those sources for copies of the full syllabus, assignments, grading rubrics, submission links, and other useful information. If you are not enrolled in the course but would like to request access to those materials, please send me an email introducing yourself and explaining why you would like to have access to them.

Happy studying!