CSCI 544: Applied Natural Language Processing — Fall 2019

Course objectives: Welcome! This course is designed to
introduce you to some of the problems and solutions of NLP, and their
relation to linguistics and statistics. You need to know how to
program and use common data structures.
It might also be nice—though it's not required—to have
some previous familiarity with linear algebra and probabilities.
At the end you should agree (I hope!)
that language is subtle and interesting, feel some ownership over some
of NLP's formal and statistical techniques, and be able to understand
research papers in the field.
Lectures:  WF 5:30  7:20pm 
Location:  WPH B27. 
Prof:  Nanyun (Violet) Peng Email: npeng@isi.edu 
TAs:  Rujun Han Email: rujunhan@isi.edu 
Graders:  Sachin Vorkady Balakrishna, Email: vorkadyb@usc.edu; Anisha Jagadeesh Prasad, Email: ajagadee@usc.edu, Jiawei Zhang, Email: zhan890@usc.edu 
Office hrs: 
Prof: Wed. 4:30pm at RTH 512; or by appt TAs: Fri. 1:00pm  3:00pm 
Discussion site: 
Piazza
https://piazza.com/usc/fall2019/csci544/home
... public questions, discussion, announcements 
Web page:  https://violetpeng.github.io/cs544_fa19.html 
Textbook: 
Jurafsky & Martin, 3rd ed. (recommended) Manning & Schütze (recommended) 
Policies: 
Grading: homework 40%, project 20%, midterm 15% or 25%, final 15% or 25% Honesty: Viterbi integrity code, USCViterbi graduate policies 
Warning: The schedule below may change. Links to future lectures and assignments are just placeholders and will change.
Week  Wednesday  Friday  Suggested Reading  
8/26 
Introduction

Probability concepts



9/2 
Modeling grammaticality; Ngram language models

Smoothing ngrams



9/9 
Assignment 1 given: Probabilities Intro to neural language Models 
No class (SoCal NLP symposium)



9/16 
Contextfree parsing

Guest Lecture from TA:



9/23 
Assignment 1 due Assignment 2 given: Language Models Probabilistic parsing 
Dependency Parser

 
9/30 
Semantics

Midterm review 


10/7 
Midterm exam (5:306:30 in classroom) 
Distributional semantics (word embeddings)



10/14 
Sequence tagging models

Project proposal due No class (fall break) 


10/21
Assignment 2 due Assignment 3 given: Semantics 
HMM, MEMM, and CRF

Neural sequence tagging and relation extraction



10/28 
Dialog systems

Text classification


11/4
Assignment 3 due Assignment 4 given: Neural Sequence Tagging 
Project proposal revision (if applicable) due 
Machine Translation


11/11 
Phrasebased machine translation

Sequence to sequence models

 
11/18 
NLP Applications: Creative Generation

Socially responsible NLP



11/25 
No class (Thanksgiving break) 
Assignment 4 due No class (Thanksgiving break) 

12/2 
Final Project Due Final exam recitation I 
Final exam recitation II Final exam: Wed 12/11, in class. 