![]() ![]() You will often find words that are of the same edit distance from a given target word - you will need to rely on some form of "popularity metric" to rank those tied corrections. The other interesting thing for me personally about this problem is that there is a statistical angle as well. By using a trie, you eliminate lot of unwanted look-ups that Peter Norvig's brute-force approach takes. While it's clever and concise, in terms of raw speed, indexing your vocabulary in a Trie, and then doing a traversal on it using a Levenshtein distance is way faster. If you are looking to implement spelling correction, you cannot but not stumble on this excellent post by Peter Norvig (most of it written on a bored flight journey!). This has been an area of interest for me as part of working on Typesense. The difference would be on the level of a standard web-renderer's line-at-a-time reflow algorithm, vs. If speech-to-text algorithms would buffer the entire audio stream for a dictated document, showing an estimate of the output text so far, but continuing to re-estimate the entire document after every word/sentence/paragraph, they'd perform much better. ![]() It's why they fail to recognize names, for example. (Side-note: I'm surprised that speech-to-text algorithms still work mostly in "real time" with only a limited buffer, unable to go back and change anything more than a few words in the past. whereas this poem is more like the result of a very naive speech to text algorithm, one which parses each word independently without the context of the sentence-so-far. "yjr" should become "the" because it's the same letters all shifted over by one.Īnd, as in the submitted article, the best autocorrectors just look at the whole sentence context and try to predict what word the input "should" have been given what it looks like-this is essentially a kind of compressed sensing (like fMRIs use!), though in practice it tends to be baked down to something like markov chains of levenstein automata with back-propagation on w>n. Slightly-less-naive autocorrect takes this approach further, and understands that e.g. Naive spelling correctors correct by text distance, because people almost always make mistakes in text input by typing the right words but making the wrong motions to do so. But then it checks against that language only, does not help if you constantly write multiple languages or if you don't want any kind of spell checking.Obligatory party-pooping: this doesn't really seem to be a poem about a spelling checker or corrector. Note: There is an option to change the language to other than "device language" which changes the UI language as well. If I find a solution I'll post it for others.
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