Natural Language Processing
TAs : Ali Emami, Jad Kabbara, Kian Kenyon-Dean, Krtin Kumar
This course presents an introduction to the computational modelling of natural language. Topics covered include: computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. We will consider selected applications such as automatic summarization, machine translation, and speech processing. We will also study machine learning algorithms that are used in natural language processing.
Prerequisites: MATH 323 or ECSE 305; COMP 251 or COMP 252.
Useful but not required: Background in artificial intelligence (e.g., COMP 424); introductory course in linguistics (LING 201).
- TA office hours for A1: 2pm-4pm on Thursday, September 28th, in Trottier 3104.
- Because enrollment is now beyond the seating capacity of the lecture hall, I ask that auditors not attend lectures for the first two weeks. I expect that enrollment will stabilize by then, and that there will be room in the lecture hall. Lectures will not be recorded, as the room is not equipped for such.
- No office hours Sept 12. Please send me e-mail regarding any issues.
Lectures and Readings
Draft chapters of the 3rd edition of Jurafsky and Martin are available here.
|Sept 5||1 - Introduction to Natural Language Processing||J&M Ch 1 (both 1st ed and 2nd ed)|
|Sept 7||2 - Morphology, FSAs and FSTs – Lecture by Krtin Kumar||J&M Ch 2.2, Ch 3 (both 1st ed and 2nd ed)|
|Sept 12||3 - Article prediction, Python intro – Lecture by Jad Kabbara||NLTK|
|Sept 14||4 - Language models and N-grams||J&M Ch 6.1, 6.2 (1st ed); J&M Ch 4.1 – 4.4 (2nd or 3rd ed)|
|Sept 19||5 - Smoothing and model complexity||J&M Ch 6.3 (1st ed); J&M Ch 4.5 (2nd ed)
Notes by Kevin Murphy
|Sept 21||6 - Feature extraction and classification|
|Sept 26||7 - Part of speech tagging: Markov chains and hidden Markov models||J&M Ch. 8.1–8.3 (1st ed); J&M Ch. 5.1–5.3 (2nd ed)|
|Sept 28||8 - Part of speech tagging: Algorithms||J&M Ch. 7.2-7.3, 8.5 (1st ed); J&M Ch. 5.5, 6.1–6.5 (2nd ed)|
|Oct 3||9 - Linear-chain conditional random fields||Tutorial by Sutton and McCallum. Sections 1, 2–2.3, 3–3.1|
|Oct 5||10 - Recurrent neural networks|