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 
CAs:  Sachin Vorkady Balakrishna, Email: vorkadyb@usc.edu; Anisha Jagadeesh Prasad, Email: ajagadee@usc.edu 
Office hrs: 
Prof: Wed. 4:30pm; 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
(ppt)

Guest Lecture from TA: Deep Learning workshop, intro to pytorch 


9/23 
Assignment 1 due Assignment 2 given: Language Models Earley's algorithm (ppt) 
Probabilistic parsing
(ppt)

 
9/30  Midterm recitation 
Midterm exam (5:306:30 in classroom) 


10/7 
Semantics
(ppt)

Assignment 2 due Distributional semantics (word embeddings) 


10/14 
Project proposal due Assignment 3 given: Semantics Sequence tagging models (ppt) (Excel spreadsheet; Viterbi version; lesson plan; video lecture) 
No class (fall break)  
10/21 
Expectation Maximization
(ppt)

Neural sequence tagging models
(ppt)



10/28 
Assignment 3 due Assignment 4 given: Neural Sequence Tagging Guest Lecture (TBD) 
Text classification
(ppt)


11/4 
Project proposal revision (if applicable) due Guest Lecture (TBD) 
Neural text classification
(ppt)


11/11 
Assignment 4 due Morphology and phonology (ppt) 
Multilinguality and machine translation
(ppt)

 
11/18 
Sequence to sequence models
(ppt)

Current NLP tasks and competitions
(ppt)



11/25 
No class (Thanksgiving break) 
No class (Thanksgiving break) 

12/2 
Project final report due Applied NLP continued 
Final exam recitation Final exam: Wed 12/11, TBD 