CS 4705: Introduction to Natural Language Processing, Fall 2019

Course Information

Time MW 1:10-2:25pm
Place 451 Computer Science Building
Professor Kathleen McKeown
Office Hours M 2:30-3:30, W 5-6, 722 CEPSR
Email kathy@cs.columbia.edu
Phone 212-939-7114

Weekly TA hours are listed below. All TA hours will be held in the NLP lab (7LW1 CEPSR).

Monday Alyssa Hwang ahh2143@columbia.edu 3:30-5:30pm
Tuesday Elsbeth Turcan (Head TA) eturcan@cs.columbia.edu 11am-1pm
Wednesday Katy Gero katy@cs.columbia.edu 11am-1pm
Thursday Emily Allaway eallaway@cs.columbia.edu 2-4pm
Friday Fei-Tzin Lee feitzin@cs.columbia.edu 4:30-6:30pm

Here is where to find these rooms on the 7th floor of CEPSR.

Course Description

This course provides an introduction to the field of natural language processing (NLP). We will learn how to create systems that can analyze, understand and produce language. We will begin by discussing machine learning methods for NLP as well as core NLP, such as language modeling, part of speech tagging and parsing. We will also discuss applications such as information extraction, machine translation, text generation and automatic summarization. The course will primarily cover statistical and machine learning based approaches to language processing, but it will also introduce the use of linguistic concepts that play a role. We will study machine learning methods currently used in NLP, including supervised machine learning, hidden markov models, and neural networks. Homework assignments will include both written components and programming assignments.


Four homework assignments, a midterm and a final exam. Each student in the course is allowed a total of 4 late days on homeworks with no questions asked; after that, 10% per late day will be deducted from the homework grade, unless you have a note from your doctor. Do not use these up early! Save them for real emergencies.

We will use Google Cloud for the course. Instructions for setting up the cloud can be found here.


Required: Natural Language Processing, by Jacob Eisenstein. It is available on github but you can also purchase the final hard copy version from MIT Press. It should be available on Amazon.

Recommended: Speech and Language Processing, 3rd Edition, by Jurafsky and Martin.

Recommended: Neural Network Methods for Natural Language Processing by Yoav Goldberg. It is available online but you can also purchase a hard copy from the publisher.


This syllabus is still subject to change. Readings may change. But it will give you a good idea of what we will cover.

Week Class Topic Reading Assignments
1 Sep 4 Introduction and Course Overview Ch 1, NLP HW0 (DUE SEPT 7th 10AM)
Provided code
2 Sep 9 Language modeling Ch 6 through 6.2, NLP
Sep 11 Supervised machine learning, text classification Ch 2.1-2.6, 2.8, NLP HW1: Stance Detection
HW1 Data
3 Sep 16 Supervised machine learning, Scikit Learn Tutorial C 4.4, 4.5 NLP
Sep 18 Neural Nets C 3, NLP
4 Sep 23 Distributional Hypothesis and Word Embeddings C 14.2, 14.3, NLP
Sep 25 Neural nets and sentiment analysis C 4.2 NLP HW1 due
5 Sep 30 POS tagging C 8.1-8.3 Speech and Language, 8.1 NLP HW2: Emotion Detection Provided code
Oct 2 Methods: Hidden Markov Models C 7 (through 7.5) NLP; C 8.4 Speech and Language
6 Oct 7 Syntax C 11-11.1, Speech and Language; 10.2, 11-11.1, NLP
Oct 9 Dependency Parsing C 11.2-11.4 NLP
7 Oct 14 Introduction to Semantics C 19-19.3, Speech and Language HW 2 due
Oct 16 Semantics and Midterm Review C 4.2, NLP Sample Midterm Questions Sample Midterm Questions and Answers
8 Oct 21 Midterm
Oct 23 Advanced Word embeddings and semantics Reference papers HW3: Word embeddings and semantics Provided code
9 Oct 28 Word Sense Disambiguation C 13 NLP
Oct 30 Machine Translation C 18.1-18.2 NLP
10 Nov 4 Academic Holiday Academic Holiday, no classes
Nov 6 Neural MT C 18.3-18.5 NLP
11 Nov 11 Summarization Extractive Neural Net Approach
Nov 13 Summarization Abstractive Neural net approach HW 3 due
HW4: Sequence to Sequence Modeling
Provided code
12 Nov 18 Language Generation Seq2seq language generation
Nov 20 Dialog Dialog Guest speaker: Or Biran
13 Nov 25 Bias Bias in word embeddings
Nov 27 Academic Holiday Academic Holiday, no classes
14 Dec 2 Information Extraction Neural Nets, C. 17.4, 17.5.1
Dec 4 Research and Review Sample Final Questions HW4 due
15 Dec 9 In-class Final Exam


Check Piazza for announcements and check courseworks for your grades (only you will see them), and discussion. All questions should be posted through Piazza instead of emailing Professor McKeown or the TAs. They will monitor the discussion lists to answer questions.

Academic Integrity

Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is not allowed, and will result in an automatic grade of 0 for the entire assignment or exam in which the copying or paraphrasing was done. Your grade should reflect your own work. If you believe you are going to have trouble completing an assignment, please talk to the instructor or TA in advance of the due date.