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

Which option is *not* one of the 9 generally accepted parts of speech?

Which option is not one of the 9 generally accepted parts of speech?

It ensures all outputs conform to certain parts of speech.

It categorizes every tag during training using the Penn Treebank.

It ensures all of our training data is tagged before being fed into our model.

What purpose does a tagset serve in a POS tagger?

What purpose does a tagset serve in a POS tagger?

By helping identify potential grammatical errors by highlighting inconsistencies or incorrect word usage based on the expected part of speech.

By ensuring that adjectives are appropriately placed before the nouns they modify.

By comparing the part of speech tags of the subject and the verb to ensure they match in terms of number and person.

By comparing the part of speech tags of the subject and predicate of a sentence to determine improper voice.

In which way is POS tagging not relevant to grammar correction?

In which way is POS tagging not relevant to grammar correction?

A set of hidden states that generate the observations.

A sequence of observations or outputs that are emitted from the hidden states.

A state transition matrix that stores the label representing the state that gave us the maximal probability.

A maximal probability matrix that encodes the maximal probabilities of transitioning from one hidden state to another.

An emission matrix that specifies the probabilities of emitting particular observations from each hidden state.

What is not a component of an HMM?

$C(i,j) = \text{max}(C(k,j-1) \times A(k,i) \times B(i,w))$

$C(i,j) = \text{argmax}(C(k,j-1) \times A(k,i) \times B(i,w))$

$C(i,j) = \text{max}(C(i,j-1) \times A(i,j) \times B(i,j))$

$C(i,j) = \text{argmax}(C(k,j) \times A(k-1,i) \times B(i-1,w))$

During the forward pass of the Viterbi Algorithm, how is the value in matrix C calculated? (assuming C and D are both $i \times  j$, $k$ is the corresponding tag, and $w$ is the corresponding word)

During the forward pass of the Viterbi Algorithm, how is the value in matrix C calculated? (assuming C and D are both <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi><mo>×</mo><mi>j</mi></mrow><annotation encoding="application/x-tex">i \times j</annotation></semantics></math>i×j, <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi></mrow><annotation encoding="application/x-tex">k</annotation></semantics></math>k is the corresponding tag, and <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>w</mi></mrow><annotation encoding="application/x-tex">w</annotation></semantics></math>w is the corresponding word)

Test your knowledge of part-of-speech tagging 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: Part-of-Speech Tagging