01/18 Lecture 01
Introduction
What is NLP? What will you learn in this class? How will we teach this class?
01/20 Lecture 02
Corpora, Words, Tokenization
What is a corpus? What are words? How do we identify them?
Reading: Ch. 2
Week 1 Assignments
01/20–02/05 Quiz 1
01/20–02/05 PG Assignment 1
01/25 Lecture 03
Morphology and Finite-State Methods
The structure of words; finite-state transducers
Reading: Ch. 2
01/27 Lecture 04
Probabilistic modeling in NLP; N-Gram Language Models
Review of basic probability. How do we apply these ideas to NLP? N-gram language models
Reading: Ch. 3
Week 2 Assignments
01/27–02/12 Quiz 2
01/27–02/19 HW1
02/01 Lecture 05
Text Classification with Naive Bayes
Introduction to classification with probabilistic models.
Reading: Ch. 4
02/03 Lecture 06
Text Classification with Logistic Regression
Introduction to conditional probabilistic models for classification
Reading: Ch. 5
Week 3 Assignments
02/03–02/19 Quiz 3
02/03–02/19 PG Assignment 2
02/08 Lecture 07
Lexical Semantics
How do we represent the meaning of words?
02/10 Lecture 08
Vector Semantics and Embeddings
Representing words as sparse or dense vectors
Reading: Ch. 6
Week 4 Assignments
02/10–02/26 Quiz 4
02/10–02/26 PG Assignment 3
02/15 Lecture 09
Feedforward Neural Nets
Basic neural nets for classification and language modeling
Reading: Ch. 7
02/17 Lecture 10
Convolutional Neural Nets for NLP
Another neural architecture for (text) classification
TBA
Week 5 Assignments
02/17–03/05 Quiz 5
02/17–03/12 HW 2
02/22 Lecture 11
Part-of-Speech Tagging
What are parts-of-speech? Introduction to HMMs
Reading: Ch. 8
Week 6 Assignments
02/24–03/12 Quiz 6
02/24–03/12 PGA 4
03/01 Lecture 13
Recurrent Neural Nets for Sequence Modeling
Introduction to RNNs and neural sequence processing
Reading: Ch. 9
03/03 Lecture 14
More on Neural Approaches to Sequence Modeling
Attention, Transformers
Reading: Ch. 10
Week 7 Assignments
03/03–03/26 Quiz 7
03/03–03/26 PGA 5
03/08 Lecture 15
Fine-Tuning and Masked Language Modeling
Large Language Models, Contextual Embeddings
Reading: Ch. 11
03/10 Lecture 16
Machine Translation
Why is machine translation difficult? Statistical and Neural Models for MT
Reading: Ch. 13
Week 8 Assignments
03/10–04/02 Quiz 8
03/10–04/09 HW 3
03/22 Lecture 17
Constituency Grammars and Parsing
Context-free grammars for English, CKY parsing, Penn Treebank
Reading: Ch. 17
03/24 Lecture 18
Dependency Grammars and Parsing
Dependency Trees, Universal Dependencies, Shift-Reduce Parsing
Reading: Ch. 18
Week 9 Assignments
03/24–04/09 Quiz 9
03/24–04/09 PGA 6
03/29 Lecture 19
Expressive Grammars
Going beyond CFGs (with a focus on categorial grammars)
Optional Reading: Steedman
& Baldridge (2011)
03/31 Lecture 20
Compositional Semantics
What is the meaning of a sentence, and how can we represent it? Basic predicate logic and lambda calculus
Reading: Ch. 16
Week 10 Assignments
03/31–04/16 Quiz 10
03/31–04/16 PGA 7
04/05 Lecture 21
Semantic Role Labeling
How do we represent and capture who does what to whom?<
Reading: Ch. 24
04/07 Lecture 22
Referring Expressions and Coreference Resolution
How do we refer to entities in text? How do we identify the same mentions of the same entities?
Reading: Ch. 26
Week 11 Assignments
04/07–04/23 Quiz 11
04/07–04/30 HW 4
04/09 Literature Review Draft due (4th Credit hour)
04/14 Lecture 24
Discourse Coherence
Going beyond sentences: what makes longer texts coherent and cohesive?
Reading: Ch. 27
Week 12 Assignments
04/14–04/30 Quiz 12
04/14–04/30 PGA 8
04/21 Lecture 26
Dialogue and Chatbots
Properties of human conversation, chatbots vs. dialogue systems
Reading: Ch. 15
Week 13 Assignments
No new assignments this week
Week 14 Assignments
04/30 Literature Review due (4th Credit Hour)
05/03 Lecture 29
TBD
TBD
Reading: TBD