3 ms·

Thanks for the friendly correction! I am no native speaker and every friendly correction is helpful, of course! Trouble with spelling correction: It does not

Thanks for the friendly correction! I am no native speaker and every friendly correction is helpful, of course! Trouble with spelling correction: It does not understand context and thus lets you run into wrong wording ....

pbhjpbhj 13y ago
Meta: I've mentioned it before here - why is standard spell-checking so lame. The phrase "It seams that" is only going to be correct about 1 in a few-million times ["if you bend it seams that have high pressure applied will burst", maybe]. If I was clever enough I could probably fix it; I've heard that phrase based analysis is used for translation. Most homophonic spelling errors seem fixable using automated lexical context analysis. Seems there is movement in mobile spell-check and word suggestion but not in desktop?
- dkuntz2 13y ago
  One of the Senior Thesis presentations at my college last year was someone working on a context spell-checker. I don't know if his demo could suggest a better word, but it would highlight words that seemed wrong. It was based off of Google's NGrams, I think he used 3-grams, and checked to see how frequently a word showed up between the two words next to it. The problem with that was it required a HUGE amount of data. Like several hundred gigabytes worth of space just to store the 3-grams (compressed down to one instance of each 3-gram coupled with the number of times it showed up in the original dataset).
  - pbhjpbhj 13y ago
    Average vocabulary isn't going to require using that massive a set of 3-grams though. If we started with homophones (https://en.wikipedia.org/wiki/Homophone#English https://en.wikipedia.org/wiki/Homophone#English) that would seem to make a large difference; add in ability to easily get a definition (or list of synonyms) for words/phrases (like in Google Translate). Google suggest already does a lot of what is required.
    - dkuntz2 13y ago
      Google also has a ton of computing power and disk space... That said, homophones and homonyms only would make the 3-gram sets smaller, but it would only detect homophones and homonyms being used incorrectly. I could still use a word incorrectly, and if I'm banking on that software to detect my mistakes it wouldn't.
      - pbhjpbhj 13y ago
        Indeed. My false positive rate [flagged errors that are really omissions from the dict.] on spell-check in-browser appears to have been about 95% over the past couple of months. I wouldn't be expecting perfection; surely some mistakes corrected is better than none. How I envision it is also a learning tool - "can here you" would pop up a "can {here} you [here refers to location, hear to hearing sound; 'can hear you']" allowing the author to click the 'corrected' phrase. At the same time you're learning the distinctions. Thanks for the input.

pbhjpbhj 13y ago

Meta: I've mentioned it before here - why is standard spell-checking so lame. The phrase "It seams that" is only going to be correct about 1 in a few-million times ["if you bend it seams that have high pressure applied will burst", maybe]. If I was clever enough I could probably fix it; I've heard that phrase based analysis is used for translation. Most homophonic spelling errors seem fixable using automated lexical context analysis. Seems there is movement in mobile spell-check and word suggestion but not in desktop?

dkuntz2 13y ago
One of the Senior Thesis presentations at my college last year was someone working on a context spell-checker. I don't know if his demo could suggest a better word, but it would highlight words that seemed wrong. It was based off of Google's NGrams, I think he used 3-grams, and checked to see how frequently a word showed up between the two words next to it. The problem with that was it required a HUGE amount of data. Like several hundred gigabytes worth of space just to store the 3-grams (compressed down to one instance of each 3-gram coupled with the number of times it showed up in the original dataset).
- pbhjpbhj 13y ago
  Average vocabulary isn't going to require using that massive a set of 3-grams though. If we started with homophones (https://en.wikipedia.org/wiki/Homophone#English https://en.wikipedia.org/wiki/Homophone#English) that would seem to make a large difference; add in ability to easily get a definition (or list of synonyms) for words/phrases (like in Google Translate). Google suggest already does a lot of what is required.
  - dkuntz2 13y ago
    Google also has a ton of computing power and disk space... That said, homophones and homonyms only would make the 3-gram sets smaller, but it would only detect homophones and homonyms being used incorrectly. I could still use a word incorrectly, and if I'm banking on that software to detect my mistakes it wouldn't.
    - pbhjpbhj 13y ago
      Indeed. My false positive rate [flagged errors that are really omissions from the dict.] on spell-check in-browser appears to have been about 95% over the past couple of months. I wouldn't be expecting perfection; surely some mistakes corrected is better than none. How I envision it is also a learning tool - "can here you" would pop up a "can {here} you [here refers to location, hear to hearing sound; 'can hear you']" allowing the author to click the 'corrected' phrase. At the same time you're learning the distinctions. Thanks for the input.