Machine Learning and Natural Language

Spring 2016

Course Plan and Lecture Notes

Note: Topics, Lecture Notes, Relevant Papers and Presentations will be made available and will be updated throughout the semester.
Papers that are recommended for presentation are denoted by P along with their category

        I.    Introduction

  1. Introduction to the Class [PPT] [PDF] (01/20)
    1. NLP Problems; Key Approaches

  2. Models of Classification and Multiclass Classification (01/27)
  3. Reading:

        II.    Basic Structured Models: Sequential Models

  4. Sequence Labeling Problems (2/3, 2/10)

  5. (02/18, 02/23, 02/25)
    1. HMMs and CRFs
    2. Inference with Classifiers I
    3. Structured Perceptron
    4. Structured SVMs


        III.    Constrained Conditional Models

    1. Pipeline Models
    2. Integer Linear Programming
    3. Introducing Background knowledge


        IV.    Training Paradigms

    1. Decoupling Learning from Inference (L+I)
    2. Inference based Training (Joint Learning, IBT)
    3. Online and Batch Joint Learning


        V.    Unsupervised Learning and Indirect Supervision

    1. Constraints Driven Learning and Posterior Regularization
    2. Learning with latent variables
    3. Indirect Supervision


        VI.    Inference

    1. Approximate Inference
    2. Dual Decomposition


    Group Presentations Schedule (Tentative: order of the groups is pretty firm)

    Tentative Presentation Schedule (google doc)