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Readings should be done before the indicated class.

Most slides have keynote format available if you change .pdf to .key in the url.

“JM” sometimes refers to 2nd edition, sometimes 3rd edition, of the Jurafsky and Martin text.

Tu 9/8 - Introduction [slides pdf]

Th 9/10 - Probability and Naive Bayes [slides pdf]


Optional resources:

Weekend assignment: install Python and get comfortable with it.

Tu 9/15 - More Classification [slides pdf]

HW1 is out. Due Friday Sept 25. Files:

Readings for today:

Python demo notebook HTML from lecture. (Also ipynb format)

Th 9/17 - Logistic Regression [slides pdf]


Tu 9/22 - Guest Lecture by Gaja Jarosz


Th 9/24 - Part-of-speech tags [slides pdf]


F 9/25: HW1 is due.

Tu 9/29 - HMM and Viterbi

Lecture notes on HMM and Viterbi


Th 10/1 - Discriminative sequence models, part 1 [slides pdf]

Log-linear models, Conditional Random Fields (which are a type of log-linear model).

In-class exercise 3: additive Viterbi


HW2. Due Oct. 13 at midnight.

Tu 10/6 - Discriminative sequence models, part 2 [slides pdf]

Review Viterbi, and do Structured Perceptron learning.

Scan of in-class lecture scribblings

Th 10/8 - Projects discussion [slides]

Also board photos and notes on Viterbi, factor scores as a graph, and Problem 3

No class Tu 10/13

Tue 10/13: HW2 due

Th 10/15 - Midterm Review

Tu 10/20 - In-class Midterm

Th 10/22 - Edit distance and the noisy channel [slides]


Tu 10/27: Machine Translation, Part 1 [slides]


Th 10/29: Machine Translation, Part 2 [slides]


HW3: due Friday 11/6 (at any time, or later that night)

Tu 11/3: Human Evaluation (and finish MT)


Th 11/5: Sigtesting and Parsing, part 1


Tu 11/10: Parsing, part 2


Th 11/12: Parsing, part 3

Reading is Ch 13 again.

Tu 11/17: Dependencies and Coreference, Part 1


Th 11/19: Coreference, Part 2

HW4 coreference: hw4.pdf and hw4.zip. Due Friday 12/4 (at any time, or later that night)

Tu 11/24: Lexical semantics [slides]

Suggested reading: JM 2ed, Chapters 19 and 20

(11/26: Thanksgiving)

Tu 12/1: Distributional semantics [slides]

Suggested reading: Turney and Pantel 2010, “From Frequency to Meaning: Vector Space Models of Semantics”

HW5 on distributional similarity

Th 12/3: Topic models and neural networks in NLP

Tu 12/8 and Th 12/10: Final presentations

See the projects page for details.