A Spellchecker Used To Be A Major Feat of Software Engineering

Here's an article that people might be interested in. It gives a bit more detail: https://web.archive.org/web/20100706052342/http://www.spelli... I'm particularly interested in this one, and I'm curious about how useful something like this would be to use. > The second does not use a dictionary at all (Morris & Cherry 1975). Like the previous method, it divides the text into trigrams, but it creates a table of these, noting how often each one occurs in this particular piece of text. It then…

I read an article where a software engineer was about to go on a long plane flight, so he downloaded a file of all english words and tried to write a spell checker during his flight, with no internet access, just as an exercise. And when he landed, he had finished, and his algorithm was beautiful, simple, elegant, easy to understand, and never something I'd have come up with in a million years. It was actually a little depressing -…

Is there a reason why Apple's iPhone spellcheck is often really poor, significantly worse than both LLMs and just...human eyes? I often find myself butchering the spelling of a word in a way where the correct answer is obvious to human eyes (probably because of "typoglycemia" [1]) and an AI LLM immediately understands what I meant to say, but Apple's spellcheck has "No Guesses Found." Does anyone else have this experience? 1. https://www.dictionary.com/e/typoglycemia/

While these specific challenges are trivial now because of increased memory and CPU, user expectations have increased too, requiring more advanced features which are not trivial to implement across different platforms. Users now expect good auto-suggestion when doing text entry on mobile platforms and laugh when it fails ( http://damnyouautocorrect.com/ ). I just launched an iPad app to improve communication for speech-disabled users and nearly everyone I talked to wanted word-completion and next-word suggestion…