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Lecture schedule 2008/9

Block A: Sep and Oct 2008 (Tentative Schedule: May Change During Course of Semester)
  1. Introduction and Motivation (Why probabilistic models for language and speech processing?)
    http://staff.science.uva.nl/~simaan/D-LangAndSpeech0809/D-Lectures/LSP091.pdf
    Read chapters 1 and 2 of Manning &Scheutze or Jurafsky&Martin. Also read this paper: Empirical validity and technological viability: Probabilistic models of Natural Language Processing.
  2. Basic Probability Theory and Statistics
    http://staff.science.uva.nl/~simaan/D-LangAndSpeech0809/D-Lectures/LSP092.pdf
    Main reading: chapters 1 and 2 from Manning&Scheutze
    More about statistics: Read also chapter 1 of Krenn and Samuelsson http://www.ofai.at/~brigitte.krenn/papers/stat_nlp.ps.gz
    More about learning: Read chapters 1 and 2 of Machine Learning (T. Mitchell).
    Free choice homework (no need to deliver):
    • Derive the corollaries on lecture slide 8, the chain rule on slide 9 and the partition rule on slide 10. Use only the axioms and set theory to do the derivation.
    • Let a ``word" be defined as a sequence of symbols separated by white-space.
      Take a large English text (for example a collection containing at least 1 million word occurences from Wikipedia) and extract all words and their counts from a steadily growing part of the text. To do so, start with 10% of the text and add another 10% every time until you have the full text to do the counting.
      Plot the relative frequency estimate (RFE) of the word probability for certain words (e.g. ``the" or ``man" or ``company" or ``browsing" or ``Bush") and observe whether the RFE is converging around a certain value as the data grows large. Discuss the differences between the convergence of different words with your fellow colleagues.
  3. Hidden Markov Language Models
    Word-prediction, sentence probability (without structure), Ngrams and Markov models, POS tagging and Hidden Markov Models.
    http://staff.science.uva.nl/~simaan/D-LangAndSpeech0809/D-Lectures/LSP093.pdf
    Read chapter 6 of Juranfsky and Martin or Sections 6.1-6.3 + 9.1 from Manning and Schutze.
    Read chapter 8 (Jurafsky and Martin) about POS tagging in general (you may skip section 8.6)
    On HMMs: read from chapter 9 (Manning and Schutze) only sections 9.1+9.2 +9.3.1+9.3.2)
    Further on evaluation of Taggers: read section 10.6 (Manning and Schutze).
    More on tagging see: http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=972477
  4. HMM implementation as SFST; Tagging Algorithms; Forward/Backward Algorithms
    Same slide file as preceding lecture (i.e. http://staff.science.uva.nl/~simaan/D-LangAndSpeech0809/D-Lectures/LSP093.pdf).
    See preceding lecture for details. This one extends it.
    Read also Chapter 10 of Manning and Schutze and on Spelling Correction from Jurafsky and Martin chapter 5 (till section 5.6) and chapter 6.
  5. Dealing with Unseen Events: Methods for Smoothing Maximum-Likelihood Statistics
    http://staff.science.uva.nl/~simaan/D-LangAndSpeech0809/D-Lectures/LSP095.pdf
    Read chapter 6 from Manning and Schutze (or chapter [6.1-6.6] from Jurafsky and Martin), and then until page 18 from Joshua Goodman and Stanley Chen. "An empirical study of smoothing techniques for language modeling". Technical report TR-10-98, Harvard University, August 1998.
  6. Basic Information Theory, Learning and Estimation
    Also in chapter 1,2 from Manning&Scheutze; read also chapter 1 of Krenn and Samuelsson
  7. First Parsing lecture
Block B: Nov and Dec 2008 (Tentative Schedule: May Change During Course of Semester)
  1. Lecture
  2. Lecture
  3. Lecture
  4. Lecture
  5. Lecture
  6. Lecture
  7. Lecture

next up previous
Next: Midterm project Up: Language and Speech Processing Previous: Reading material
Khalil Sima'an 2008-10-02