Published in


Applying Context Aware Spell Checking in Spark NLP

Image credit: Pexels.


Today we are exploring Spell Checking, a very important task in any serious NLP pipeline that needs to deal with noisy, incorrect data that has been generated in the wild.

Take for example the case of tweets, instant messaging, blog posts, OCR, or any other user generated text content. Being able to rely on correct data, without…




Natural Language Understanding Library for Apache Spark.

Recommended from Medium

Prediction on Customer Churn with Mobile App Behavior Data

Introduction to Neural Nets (Without the Brain Metaphor)

Regularization for machine learning in terms a child could understand.

Freesound Audio Tagging — Recognizing Sounds of Various Natures

A new handwritten digits dataset in ML town: Kannada-MNIST

Applied Natural Language Processing (NLP) in Python | Exploring NLP Libraries

Machine Learning for Starters: First Step

Modeling Cumulative Impact Part III

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Alberto Andreotti

Alberto Andreotti

NLP expert.

More from Medium

Keyword Extraction — A Benchmark of 7 Algorithms in Python

Using Lime for Interpreting NLP

The Theory Behind Naive Bayes | NLP Python Classification Example

Pre-trained Python model for Sentiment Analysis