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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.
37 Lessons
2 Projects
10h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
Introduction
Introduction
Get familiar with grammar parsing, spell checking, and essential NLP terminology in Python.
2.
Edit Distance
Edit Distance
Discover the logic behind edit distance, its algorithms, and practical NLP applications.
3.
Basic Spellchecker
Basic Spellchecker
8 Lessons
8 Lessons
Examine spellchecker construction, models like Norvig's, and improve error correction accuracy.
4.
Modern Spell Check Methods
Modern Spell Check Methods
5 Lessons
5 Lessons
Apply your skills to modern spell check methods using SymSpell, machine learning, and transformers.
5.
Part-of-Speech Tagging
Part-of-Speech Tagging
6 Lessons
6 Lessons
Map out the steps for POS tagging using HMMs, Viterbi, and NLTK tools.
6.
Basic Grammatical Error Checking
Basic Grammatical Error Checking
5 Lessons
5 Lessons
Simplify complex topics in rule-based grammar checking and POS tagging implementation.
7.
Modern Grammar Error Correction Methods
Modern Grammar Error Correction Methods
3 Lessons
3 Lessons
Practice using modern transformer-based methods for effective grammar error correction.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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 implement a POS rule-based grammar checker, using a heuristic-based approach to correct grammar mistakes. Finally, you’ll learn to develop a transformer-based spellchecker using HuggingFace’s transformer libraries through a hands-on final project.
After completing this course, you’ll fully understand how modern spellcheckers and grammar correction software work and how these can be integrated into natural language corrector systems such as Grammarly.
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