I figured they, and others, could benefit from an explanation.

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For example, P(|) is relatively high,but P(|) would be very low.

Type in a search like  and Googleinstantly comes back with Showing results for:.

The four parts of the program are:

We could create a better language model by collecting more data, and perhaps byusing a little English morphology (such as adding "ility" or "able" to the end of a word).

That gives us:Now it is time to evaluate how well this program does.

For example, if theinput is "electroencephalographicallz", a good correction would be tochange the final "z" to an "y", even though"electroencephalographically" is not in our dictionary.

For example, occurrences of "the" make up about 7% of English text, sowe should have P() = 0.07.

But we'll leave that for another day...

This is most obvious when there is a word that appearsin the dictionary, but the test set says it should be corrected toanother word anyway:We can't possibly know that should be'were' in at least one case, but should remain 'where' in other cases.

We could also decide what dialect we are trying to train for.

For example, allowing only the insertion ofa vowel next to another vowel, or the replacement of a vowel foranother vowel, or replacing close consonants like "c" to "s" wouldhandle almost all these cases.

And this list is far away of being complete!

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Consider the misspelled word ="thew" and the two candidate corrections ="the" and ="thaw".

The more serious is unknown words.

We couldachieve this with a language model based on components of words:perhaps on syllables or suffixes, but it iseasier to base it on sequences of characters: common 2-, 3- and 4-lettersequences.

Well, "thaw" seems good because the only change is "a" to "e", which is a small change.

Clearly we could use a better model of the cost of edits.

But I figured that in the course of a transcontinental plane ride I could write and explain a toyspelling corrector that achieves 80 or 90% accuracy at a processingspeed of at least 10 words per second in about half a page of code.

So we can make  produce the first non-empty list of candidates in order of priority:

Our program enumerates all corrections withinedit distance 2.

Thesemay be interesting for those who like comparinglanguages, or for those who want to borrow an implementation in theirdesired target language:This essay has been translated into:Thanks to all the authors for creating these implementations and translations.