Gain insights into building spell checkers and grammatical error correction models using Python. Delve into NLP packages, POS tagging, heuristic methods, and transformer-based spellcheckers for practical application.

Spell Check and Grammar Parsing using Natural Language Processing.png

gector.tar.gz

python3.8

gector job-spa

python 3.7 (no warning)

python3.7

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

These are task-specific functions that mimic artificial errors.

Splitting a token into fragments could be considered a g-transformation.

Changing the tense of a verb could be considered a g-transformation.

The amount of g-transformations to get from an incorrect to a correct sentence is known as the edit distance.

**(Select all that apply.)** How do g-transformations function in artificial dataset generation?

(Select all that apply.) How do g-transformations function in artificial dataset generation?

Transformer-based GEC models are often implemented as a modification to what type of adjacent NLP problem?

Transformer-based GEC models are often implemented as a modification to what type of adjacent NLP problem?

It measures the overlap of n-grams between the candidate translation and reference translations, focusing more on recall than precision.

It measures the precision of the generated translation by comparing it to one or more reference translations.

A model that considers stemming and synonymy and incorporates a penalty for unigram matches to arrive at an overall evaluation score.

An algorithm that uses an adversarial transformer model to assess model precision.

Which statement describes the purpose of ROGUE in evaluating GEC models?

Which statement describes the purpose of ROGUE in evaluating GEC models?

GECToR utilizes an iterative sequence tagging approach. 

GECToR utilizes errorful data to train  a transformer.

GECToR utilizes grammar-specific penalties in its loss function.

GECToR leverages the  leverages the sequence-to-sequence nature of transformers to learn complex grammatical structures and patterns for error correction.

How does GECToR differ from a simple transformer-based GEC model?

How does GECToR differ from a simple transformer-based GEC model?

By modifying our loss function to include penalties for misspellings of words.

To include and annotate spelling errors within our training data.

By utilizing the cross-entropy loss as a methodology for predicting spelling errors.

Typically, different models are trained for grammar correction and spelling correction, and then both are applied to the tokenized sentence.

**(Select all that apply.)** How can a model correct both grammar and spelling?

(Select all that apply.) How can a model correct both grammar and spelling?

Test your knowledge of modern grammar checkers and apply this knowledge to a brief assessment.

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

Quiz: Modern Grammar Correction Methods