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.

Spell Check and Grammar Parsing using Natural Language Processing.png

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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.

Building Grammatical Error Correction Models with Python

$$\underset{c \ \in \ \text{candidates}} { \text{ arg\ max}} \ P(c) P(w|c)$$

- **Candidate model:** The valid candidates to consider as correct spelling, $c \ \in \ \text{candidates}$.


- **Language model:** The probability  $P(c)$ that $c$ appears as a word of English text. For example, "the" makes up about $7\%$ of English text, so $P(\text{the})$ = 0.07.


- **Error model:** The probability $P(w|c)$ that $w$ would be typed in a text when the author meant $c$.


- **Selection mechanism:** Choose the candidate that results in maximizing the conditional probability,  $\text{arg \ max}$.

Review this section's material. Learn about the weaknesses of the Norvig spellchecker and some possible methods of improvement. 

Putting It All Together

Review this section's material. Learn about the weaknesses of the Norvig spellchecker and some possible methods of improvement.

Introduction

Edit Distance

Basic Spellchecker

Modern Spell Check Methods

Part-of-Speech Tagging

Basic Grammatical Error Checking

Modern Grammar Error Correction Methods

Project: Simple Transformer

Conclusion

Auto-Tagging System for Content Categorization

Putting It All Together

Review