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Applying Context Aware Spell Checking in Spark NLP

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 spelling problems reduces vocabulary sizes at different stages in the pipeline, and improves the performance of all the models in the pipeline.

Spark NLP 2.5 delivers state-of-the-art accuracy for spell checking and sentiment analysis

John Snow Labs is thrilled to announce the immediate availability of the new major version of Spark NLP 2.5 with spell checking and...