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 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
by Dongyao Hu (me)
The exercise codes are in the exercise directory. Each sub-directory is corresponding to the download directory in the original repo maintained by Graham Neubig.