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FNLP: 10: Logistic Regression / Maximum Entropy Model

This page introduces you to logistic regression (aka maximum entropy model).  We start with recalling downsides of the Naive Bayes, and develop a more powerful model.  The slides in this folder describe the model, go into details of the estimation procedure and contrast Naive Bayes and logistic regression.

Please do the required reading and attempt the quiz.

If there is anything you don't understand, then please ask questions in the lecture or on piazza.

Slides and required reading

Slides

Reading: Jurafsky and Martin 3rd (Online) edition, https://web.stanford.edu/~jurafsky/slp3/

Sections 5.1-5.7.

Quiz from lecture 9 also covers both lectures, if you have not done it, do it now.

Quiz 10: Logistic Regression

These questions are designed to test your understanding of the above course content; doing this quiz does not contribute to your overall grade.  Some questions require a text answer.  You can ask for formative feedback on these from your tutor or on piazza.  Other questions are multiple choice or they require a numeric answer: you will get immediate feedback for these. Please don't attempt this quiz until you have acquainted yourself with this lecture and the required reading.

You must be logged onto Learn to do this quiz.

License
All rights reserved The University of Edinburgh

Book traversal links for FNLP: 10: Logistic Regression / Maximum Entropy Model

  • FNLP: Week 4: More ML methods, Morphology and POS tagging
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  • FNLP: 11: Morphology

Navigation links

  • FNLP: Resource List
  • FNLP: Assessment
  • FNLP: Course Materials
    • FNLP: Week 1: Overview, Ambiguity and Corpora
    • FNLP: Week 2: Annotation, Evaluation and Language Models
    • FNLP: Week 3: Important ML techniques for NLP
    • FNLP: Week 4: More ML methods, Morphology and POS tagging
      • FNLP: 10: Logistic Regression / Maximum Entropy Model
      • FNLP: 11: Morphology
      • FNLP: 12: Part-of-Speech tagging
    • FNLP: Week 5: POS Tagging, Context Free Grammars and Parsing
    • FNLP: Week 6: More Parsing and Compositional Semantics
    • FNLP: Week 7: Discourse Semantics and Lexical Semantics
    • FNLP: Week 8: Deep Learning for NLP
    • FNLP: Week 9: Neural Text Generation
    • FNLP Week 10: Transfer learning, Revision and Q&A
  • FNLP: Lab Exercises
  • FNLP: Tutorial Exercises
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