by Graham Neubig (neubig at is dot naist.jp)
This is a tutorial to learn the basics of natural language processing and machine learning through programming exercises using Python. The tutorial files are in the "download" directory, so please open up this directory and view the PDF there.
The tutorial covers the following material:
- Tutorial 0: Programming Basics
- Tutorial 1: Unigram Language Models
- Tutorial 2: Bigram Language Models
- Tutorial 3: Word Segmentation
- Tutorial 4: Part-of-Speech Tagging with Hidden Markov Models
- Tutorial 5: The Perceptron Algorithm
- Tutorial 6: Advanced Discriminative Training
- Tutorial 7: Neural Networks
- Tutorial 8: Recurrent Neural Networks
- Tutorial 9: Topic Models
- Tutorial 10: Phrase Structure Parsing
- Tutorial 11: Dependency Parsing
- Tutorial 12: Structured Perceptron
- Tutorial 13: Search Algorithms
- Bonus 1: Kana-Kanji Conversion for Japanese Input
