|Time||TTh: 4:30-5:10||Place||TBD Mudd|
|Professor||Kathleen McKeown||Office Hours||Tu 5:30-6:30,We 4-5, 722 CEPSR|
|Teaching Assistant||Fei-Tzin Lee email@example.com||Office Hours||TBD|
|Elsbeth Turcan firstname.lastname@example.org||Office Hours||TBD|
|Siddharth Varia email@example.com||Office Hours||TBD|
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 core NLP, such as language modeling, part of speech tagging and parsing. We will also discuss applications such as information extraction, machine translation, automatic summarization, and question-answering. 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. Stay tuned on how to sign up for course credits.
Speech and Language Processing, 2nd Edition, by Jurafsky and Martin. It will be available from the University Bookstore, as well as from Amazon and other online providers. It should also be on reserve in the Engineering Library.
Neural Network Methods for Natural Language Processing by Yoav Goldberg. It is available online but you can also purchase 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.
|1||Sep 5||Introduction and Course Overview||Ch 1, Speech and Language|
|Sep 7||Language modeling||Ch 4, Speech and Language|
|2||Sep 12||Supervised machine learning, text classification||Ch 2 Neural Nets,||HW1: Republican or Democrat?|
|Sep 14||Supervised machine learning|
|3||Sep 19||Methods: Hidden Markov Modeling||C 5.1-5.5, Speech and Language|
|Sep 21||POS tagging||C 6.1-6.5 Speech and Language|
|4||Sep 26||Syntax and Grammars||C 12 Speech and Language||HW1 due|
|Sep 28||Parsing||C. 13 Speech and Language||HW2: Parsing|
|5||Oct 3||Dependency Parsing||C 14.6 Speech and Language|
|Oct 5||Supervised learning of parsers, evaluation||C 14, Speech and Language|
|6||Oct 10||Introduction to semantics||C 17 Speech and Language|
|Oct 12||Lexical Semantics, Distributed semantics||C 19, C 20.1-20.8 Speech and Language||HW 2 due|
|7||Oct 17||Semantic role labeling||C 20.9|
|8||Oct 24||Neural nets||C 3,4 Neural Nets|
|Oct 26||Neural nets||C 5 Neural Nets|
|9||Oct 31||NN example: semantic similarity||C 10,11 Neural Nets||HW3: Neural Nets (written)|
|Nov 2||NN: RNNs and Sentiment Analysis||C 14, C16.1 Neural Nets|
|10||Nov 9||Summarization||C 23.3-23.8 Speech and Language||HW3 due|
|11||Nov 14||Summarization: abstractive||papers||HW4: Summarization|
|Nov 16||Machine Translation||C 25.1 - 25.9 Speech and Language|
|12||Nov 21||Machine Translation||C 17 Neural Nets|
|13||Nov 28||NN: Image Captioning||Show and Tell|
|Nov 30||Information extraction||C 21.1 - 21.4 Speech and Language|
|14||Dec 5||Inference, entailment||Papers||HW 4 due|
|Dec 17||Poetry, dialog||Papers|
Check Piazza for announcements, 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.
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