Demos
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.
By applying context-aware spell-checking in Spark NLP, healthcare applications can significantly improve communication accuracy, enabling the seamless integration of Generative AI in Healthcare and empowering a Healthcare Chatbot to provide more reliable and efficient patient interactions.
