NLP: CS-4650/7650

MW 3:30-4:45pm, Hybrid

Course Information

This course gives an overview of modern data-driven techniques for natural language processing. The course moves from shallow bag-of-words models to richer structural representations of how words interact to create meaning. At each level, we will discuss the salient linguistic phenomena and most successful computational models. Along the way we will cover machine learning techniques which are especially relevant to natural language processing.

Slides, materials, and projects information for this iteration of NLP courses are borrowed from Jacob Eisenstein, Yulia Tsvetkov and Robert Frederking at CMU, Dan Jurafsky at Stanford, David Bamman at UC Berkeley, Noah Smith at UW, Kai-Wei Chang at UCLA.

Instructor

Diyi Yang

Teaching Assistants

Yuval Pinter (Head TA)

Sandeep Soni

Kaige Xie

Nihal Singh

Jingfeng Yang

Haard Shah

Class Meets

Mondays and Wednesdays, 3:30-4:45pm

Piazza

piazza.com/gatech/fall2020/cs7650cs4650

Staff Mailing List

cs4650-7650-f20-staff@googlegroups.com

Online Office Hours (Eastern Time)

Nihal Singh: Monday 2-3 pm.

Kaige Xie: Tuesday 6-7 pm.

Jingfeng Yang: Tuesday 10-11 pm.

Sandeep Soni: Wednesday 10-11 am.

Yuval Pinter: Thursday 10-11 am.

Haard Shah: Friday 2-3 pm.

Schedule

Note: tentative schedule is subject to change.

Grading

• 55% Homework Assignments
  • Homework 1: 11%
  • Homework 2: 11%
  • Homework 3: 11%
  • Homework 4: 11%
  • Homework 5: 11%
• 15% Midterm Exam (Take-home Exam)
• 20% Project/Survey
  • Survey Proposal: Due Oct 7th, 11:59pm ET
  • Survey Report: Due Nov 11th, 11:59pm ET
  • Survey Final: Due Nov 22nd, 11:59pm ET
• 10% Quiz

Policies

Late Policies:

Student will have a total of five late days to use when turning in homework assignments; each late day extends the deadline by 24 hours. There are no restrictions on how the late days can be used (e.g., all 5 could be used on one homework). Using late days will not affect your grade. However, homework submitted late after all late days have been used will receive no credit.

Class Policies:

Attendance will not be taken, but you are responsible for knowing what happens in every class. The instructor will try to post slides and notes online, and to share announcements, but there are no guarantees. So if you cannot attend class, make sure you check up with someone who was there.

Prerequisites

The official prerequisite for CS 4650 is CS 3510/3511, “Design and Analysis of Algorithms.” This prerequisite is essential because understanding natural language processing algorithms requires familiarity with dynamic programming, as well as automata and formal language theory: finite-state and context-free languages, NP-completeness, etc. While course prerequisites are not enforced for graduate students, prior exposure to analysis of algorithms is very strongly recommended.

Furthermore, this course assumes:

• Good coding ability, corresponding to at least a third or fourth-year undergraduate CS major. Assignments will be in Python.
• Background in basic probability, linear algebra, and calculus.
• Familiarity with machine learning is helpful but not assumed. Of particular relevance are linear classifiers: perceptron, naive Bayes, and logistic regression.

People sometimes want to take the course without having all of these prerequisites. Frequent cases are:

• Junior CS students with strong programming skills but limited theoretical and mathematical background,
• Non-CS students with strong mathematical background but limited programming experience.

Students in the first group suffer in the exam and don’t understand the lectures, and students in the second group suffer in the problem sets. My advice is to get the background material first, and then take this course.

FAQs

• The class is full. Can I still get in?

  Sorry. The course admins in CoC control this process. Please talk to them.

• I am graduating this Fall and I need this class to complete my degree requirements. What should I do?

  Talk to the advisor or graduate coordinator for your academic program. They are keeping track of your degree requirements and will work with you if you need a specific course.

• I have a question. What is the best way to reach the course staff?

  Registered students – your first point of contact is Piazza (so that other students may benefit from your questions and our answers). If you have a personal matter, email us at the class mailing list cs4650-7650-f20-staff@googlegroups.com

Related Classe (not exhaustive!)

• Introduction to NLP, CMU
• Algorithm for NLP, CMU
• Natural Language Processing, UT Austin
• Natural Language Processing, UC Berkeley
• Natural Language Processing with Deep Learning, Stanford
• Natural Language Processing, Princeton

Book

• (SLP3) Jurafsky and Martin, Speech and Language Processing, 3rd edition
• (E) Jacob Eisenstein, Natural Language Processing (2018)