Syllabus - details

Book by book

Manning and Schütze: Foundations of Statistical Natural Language processing (FSNLP):

  • Ch 1
  • Ch 2:
    • Sec 2.1 except 2.1.10,
    • the essencials from sec 2.2 up to and including 2.2.3
  • Ch 3 is considered known background and should be studied by they who lack this background
  • Ch 4
  • Ch 5, except 5.3.4
  • Ch 6:
    • Introduction
    • Sec 6.1
    • Sec 6.2 up to (but not including) Sec. 6.2.4
  • Ch 7, except 7.3-7.4
  • Ch 8:
    • Sec 8.1
    • Sec 8.5
  • Ch 14:
    • Sec 14.2
      • Introduction+
      • 14.2.1
  • Ch 15:
    • Sec 15.1-15.2
  • Ch 16:
    • Introduction (up to but not including Sec 16.1)
    • Sec 16.2
    • Sec 16.4

Nivre’s web course: Statistical Natural Language Processing (NW)

  • Lect. 1-4

Bird, Klein and Loper: Natural Language Processing with Python (NLTK)

  • Ch 1: Sec 1.1-1.3, 1.5
  • Ch 2: Sec 2.1-2.2
  • Ch 3: Sec 3.1-3.2
  • Ch 6: Everything except Sec 6.4

Manning, Raghavan and Schütze: Introduction to Information Retrieval (IIR):

  • Ch 13, except 13.2.1
  • Ch 14:
    • Introduction
    • Sec 14.1-14.3

Jurafsky and Martin: Speech and Language Processing (J&M)

  • Ch 6: Sec. 6.6-6.8 (except 6.6.4)
  • Ch 20: Sec 20.7

By subject

Basics: ”Working with texts”

  • FSNLP:Ch 1, Ch 4
  • NLTK: Ch 1, 2.1, 3.1-3.2
  • Slides from lecture 22 Aug

Probability theory

  • FSNLP: Sec 2.1 except 2.1.10
  • Nivre’s web course: Lect. 1-3

Main concepts of Entropy

  • FSNLP 2.2-2.2.3 (We do not expect all details here, but you should know formulas 2.26 and 2.36 from FSNLP and have some ideas about why entropy is an essential concepts.)

Statistics and inference

  • FSNLP 5.1-5.3.3
  • Nivre’s web course, lect. 4
  • Slides from lecture 12 Sept.
  • Could be useful to consider other sources as well

Collocations

  • FSNLP:Ch. 5, except 5.3.4

Methodology, evaluation, smoothing

  • FSNLP:
    • Ch 6  up to (but not including) Sec. 6.2.4
    • Sec 8.1
    • Ch 16: Introduction (up to 16.1)

Na?ve Bayes classification and word sense disambiguation

  • FSNLP Ch. 7, except 7.3-7.4
  • Manning, Raghavan, Schütze, IIR, Ch. 13
  • NLTK 6.1-6.3, 6.5

Vector space semantics and IR

  • FSNLP Sec 8.1, 8.5, 15.1-2
  • J&M, Sec. 20.7
  • Slides from lecture 14 Nov.

Vector space classification: Rocchio and k nearest neighbors

  • FSNLP 16.4
  • IR 14-14.3
  • Slides from lecture 14 Nov.

Vector space flat clustering: k means

  • FSNLP 14.2: intro+14.2.1
  • Slides from lecture 14 Nov.

Linear classifiers, logistic regression, maximum entropy classifiers and tagging

  • FSNLP, Ch. 16.2
  • Jurafsky&Martin, Sec. 6.6-6.8 (except 6.6.4)
  • Ratnaparkhi 1996
  • NLTK, sec. 6.6
  • Slides from lecture 21& 28 Nov.

 

 

 

 

 

Published Dec. 5, 2011 4:14 PM - Last modified Dec. 5, 2011 4:44 PM