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All LessonsFree Lessons (3)

Introduction

Course Introduction Essential Terminology

Edit Distance

What Is Edit Distance?Calculating Minimum Edit Distance Implementation of Wagner-Fischer Algorithm Distance Calculations in NLTK Distance Calculations in Fuzzy Summary: Edit Distance Quiz: Edit Distance

Basic Spellchecker

Norvig Spellchecker Candidate Model Language Model Error Model Selection Mechanism Putting It All Together Challenge: Improving Our Basic Spellchecker Quiz: Basic Spellchecker

Modern Spell Check Methods

SymSpell: Symmetric Delete Spelling Correction Machine Learning-Based Spellchecker What Is a Transformer?Transformer-Based Spell Check Quiz: Modern Spellcheck

Part-of-Speech Tagging

What Is Part-of-Speech Tagging?Hidden Markov Models Part-of-Speech Tagging Using the Viterbi Algorithm Challenge: Viterbi Forward Pass Using NLTK Part-of-Speech Tagger Quiz: Part-of-Speech Tagging

Basic Grammatical Error Checking

Part-of-Speech Tagging and Rule-Based Grammar Checking Creating Rule-Based Grammar Objects Challenge: Applying Part-of-Speech Tagging Building a Basic Grammar Checker Quiz: Basic Grammar Checker

Modern Grammar Error Correction Methods

Neural Machine Translation Grammar Error Correction with Transformer (GECToR)Quiz: Modern Grammar Correction Methods

Mini Project

Project: Simple Transformer

Conclusion

Project

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Building Grammatical Error Correction Models with Python/

Project: Simple Transformer

Project: Simple Transformer

For the final project in this course, we will use a library called Simple Transformers, built around Hugging Face’s Transformers library. This provides an abstraction of our transformer that will be easy to use and play around with. In this project, we will preprocess a reasonably sized natural language dataset, use this to train our transformer, and finally, use that transformer to correct spelling and grammar!