How do I approximate "Did you mean?" without using Google?

Question

I am aware of the duplicates of this question:

• How does the Google “Did you mean?” Algorithm work?
• How do you implement a “Did you mean”?
• ... and many others.

These questions are interested in how the algorithm actually works. My question is more like: Let's assume Google did not exist or maybe this feature did not exist and we don't have user input. How does one go about implementing an approximate version of this algorithm?

Why is this interesting?

Ok. Try typing "qualfy" into Google and it tells you:

Did you mean: qualify

Fair enough. It uses Statistical Machine Learning on data collected from billions of users to do this. But now try typing this: "Trytoreconnectyou" into Google and it tells you:

Did you mean: Try To Reconnect You

Now this is the more interesting part. How does Google determine this? Have a dictionary handy and guess the most probably words again using user input? And how does it differentiate between a misspelled word and a sentence?

Now considering that most programmers do not have access to input from billions of users, I am looking for the best approximate way to implement this algorithm and what resources are available (datasets, libraries etc.). Any suggestions?

Answer 1

Assuming you have a dictionary of words (all the words that appear in the dictionary in the worst case, all the phrases that appear in the data in your system in the best case) and that you know the relative frequency of the various words, you should be able to reasonably guess at what the user meant via some combination of the similarity of the word and the number of hits for the similar word. The weights obviously require a bit of trial and error, but generally the user will be more interested in a popular result that is a bit linguistically further away from the string they entered than in a valid word that is linguistically closer but only has one or two hits in your system.

The second case should be a bit more straightforward. You find all the valid words that begin the string ("T" is invalid, "Tr" is invalid, "Try" is a word, "Tryt" is not a word, etc.) and for each valid word, you repeat the algorithm for the remaining string. This should be pretty quick assuming your dictionary is indexed. If you find a result where you are able to decompose the long string into a set of valid words with no remaining characters, that's what you recommend. Of course, if you're Google, you probably modify the algorithm to look for substrings that are reasonably close typos to actual words and you have some logic to handle cases where a string can be read multiple ways with a loose enough spellcheck (possibly using the number of results to break the tie).

Answer 2

From the horse's mouth: How to Write a Spelling Corrector

The interesting thing here is how you don't need a bunch of query logs to approximate the algorithm. You can use a corpus of mostly-correct text (like a bunch of books from Project Gutenberg).