Building Grammatical Error Correction Models with Python

Learn to build spell checkers and grammar correction models using Python. Explore NLP packages, POS tagging, heuristic methods, and transformer-based spellcheckers for practical use.

Intermediate

37 Lessons

10h

Certificate of Completion

Learn to build spell checkers and grammar correction models using Python. Explore NLP packages, POS tagging, heuristic methods, and transformer-based spellcheckers for practical use.

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Explanations

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Explanations

This course includes

1 Project

12 Playgrounds

11 Challenges

12 Quizzes

This course includes

1 Project

12 Playgrounds

11 Challenges

12 Quizzes

Course Overview

In this course, you’ll learn the details of spell check and grammatical error correction systems by creating them with their basic building blocks. You’ll explore natural language processing packages like NLTK, pandas, spaCy, Fuzz, GECToR, HuggingFace, and more. You’ll build the Norvig spellchecker and understand how modern machine learning-based spellcheckers work. This is followed by the mathematical concepts required for identifying part-of-speech (POS) tags for grammatical error checking. You’ll then ...Show More

What You'll Learn

An understanding of natural language distance techniques and how these are used in spell check systems

Hands-on experience building spellcheckers from scratch

An understanding of part-of-speech tagging and how it’s utilized in building grammar correction systems

An understanding of neural machine translation as a methodology for building modern grammar correction systems

Hands-on experience building transformer-based grammar correction systems using HuggingFace

What You'll Learn

An understanding of natural language distance techniques and how these are used in spell check systems

Course Content

Introduction

Get familiar with grammar parsing, spell checking, and essential NLP terminology in Python.

Course Introduction

Essential Terminology

Edit Distance

Discover the logic behind edit distance, its algorithms, and practical NLP applications.

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

Examine spellchecker construction, models like Norvig's, and improve error correction accuracy.

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

Apply your skills to modern spell check methods using SymSpell, machine learning, and transformers.

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

Part-of-Speech Tagging

Map out the steps for POS tagging using HMMs, Viterbi, and NLTK tools.

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

5 Lessons

Simplify complex topics in rule-based grammar checking and POS tagging implementation.

Modern Grammar Error Correction Methods

3 Lessons

Practice using modern transformer-based methods for effective grammar error correction.

Project: Simple Transformer

Project

Conclusion

1 Lesson

Step through the essential techniques and methods for developing grammatical error correction models.

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Project

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