This page contains some of the slides that I've made for tutorials, etc., generally aimed at the Masters or early PhD students in our lab. If you have any questions, corrections, or comments I'd be glad to hear them. If you'd like me to present on these (or something similar), send me an email, I like doing tutorials!
- Intelligent System Design: Machine Translation (10/23)
NLP Programming Tutorial
This is a tutorial I did at NAIST for people to start learning how to program basic algorithms for natural language processing. You should need very little programming experience to start out, but each of the tutorials builds on the stuff from the previous tutorials, so it is highly recommended that you do them in order. You can also download the data for the practice exercises.
- Tutorial 0: Programming Basics
- Tutorial 1: Unigram Language Models
- Tutorial 2: Bigram Language Models
- Tutorial 3: The Perceptron Algorithm
- Tutorial 4: Word Segmentation
- Tutorial 5: Part-of-Speech Tagging with Hidden Markov Models
- Tutorial 6: Kana-Kanji Conversion for Japanese Input
- Tutorial 7: Topic Models
- Tutorial 8: Phrase Structure Parsing
- Tutorial 9: Advanced Discriminative Training
- Tutorial 10: Neural Networks
- Tutorial 11: Structured Perceptron
- Tutorial 12: Dependency Parsing
- Tutorial 13: Search Algorithms
Building a Phrase-Based Machine Translation System
This tutorial by (by me and Kevin) covers the many steps that are involved in building a phrase-based machine translation system, specifically what goes on when training a system using Moses. For each of the many steps, it describes the processing that occurs, open source tools that can be used to do this porcessing, and some of the existing research problems.
Bayesian Non-Parametrics Tutorial
This is a simple tutorial about non-parametric Bayesian techniques consisting of several parts. The first part discusses the basic motivations and theory behind the use of Bayesian non-parametrics. The second part demonstrates how to implement unsupervised part of speech induction using Gibbs sampling and the Bayesian HMM, followed by an explanation of how to go from the finite HMM to the infinite HMM. Finally, the tutorial covers some more advanced topics and applications proposed in the recent literature.