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This course provides an introductory overview of the field of natural language processing. The goal of the field is to build technologies that will allow machines to understand human langauges. Applications include machine translation, automatic summarization, question answering systems, and dialog systems.

Prerequisits: You should be comfortable with linear alegbra and probability. Previous machine learning expierence is recommended, but not required.

You do not need to email the instructor to receive a permit, instead you should sign yourself up for the waitlist After you’ve added yourself to the waitlist, the instructor can issue you a permit. You will receive an email saying permit available. You will receive another email when the permit is issued. At that point, you may register on CoursesInTouch.

Homework 4 - Neural Language Models has been released. –> It is due before 11:59PM on Tuesday, November 2, 2021.
The Term Project has been released. –> The assignment has multiple deliverables.
Course number
CIS 530 - Computational Linguistics
Instructor
Mark Yatskar
Discussion Forum
Piazza
TAs will be actively watching and responding to posts Monday-Friday.
Please feel free to respond to posts as well.
If you are posting code on piazza to get help from TAs, you must follow our code posting policy
Time and place
Fall 2021, Tuesday and Thursday 3-4:30pm DRLB A4.
Lectures will be recorded and posted on Canvas.
First day of class is August 31, 2021
Last day of class is December 9, 2021
TAs
Alyssa Hwang , Rebecca A. Iglesias-Flores , Siyi Liu , Veronica Lyu, Weiqiu You
Office hours
Remote office will be run on OHQ
Monday 5:15 - 6:45 EST, Levine 612 : Siyi
Tuesday 1:30p - 3 EST, remote : Veronica
Wednesday 1:30p - 3 EST, Levine 612 : Alyssa
Thursday 10:30a - 12:00 EST, remote : Weiqiu
Friday 10a - 11:30 EST: remote, Rebecca
Friday 2p - 3 EST: Levine 402, Mark
Textbooks
Both textbooks are available for free on the web.
Speech and Language Processing (3rd edition draft) by Dan Jurafsky and James H. Martin
Natural Language Processing by Jacob Eisenstein
The course will have weekly required readings.
Grading
The grading for the course will consist of:
  • 30% for Homework 1,2,4, weighted equally.
  • 15% for Homework 3, weighted equally. (Homework 5 was removed from syllabus)
  • 15% for Quizzes, weighted equally
  • 40% for the final project, done in a group of four.

The class is not curved. Your grade will be determined as (total regular points + extra credit points) / (total regular points) using standard grade ranges.

Collaboration Policy
Unless otherwise noted, you should work in pairs on the homework assignments. Both partners will receive the same grade. The final projects will have be done in groups of 4.
Late Day Policy
Each student has ten free “late days”. Homeworks can be submitted at most three days late. If you are out of late days, then you will not be able to get credit for subsequent late assignments. One “day” is defined as anytime between 1 second and 24 hours after the homework deadline. The intent of the late day policy it to allow you to take extra time due to unforseen circumstances like illnesses. You do not need to ask permission to use your late days.