How does the Google "Did you mean?" Algorithm work?

Question

I've been developing an internal website for a portfolio management tool. There is a lot of text data, company names etc. I've been really impressed with some search engines ability to very quickly respond to queries with "Did you mean: xxxx".

I need to be able to intelligently take a user query and respond with not only raw search results but also with a "Did you mean?" response when there is a highly likely alternative answer etc

[I'm developing in ASP.NET (VB - don't hold it against me! )]

UPDATE: OK, how can I mimic this without the millions of 'unpaid users'?

• Generate typos for each 'known' or 'correct' term and perform lookups?
• Some other more elegant method?

Answer 1

I found this article some time ago: http://www.norvig.com/spell-correct.html.

It's an interesting read about the "spelling correction" topic. The examples are in Python but it's clear and simple to understand, and I think that the algorithm can be easily translated to other languages.

Answer 2

As a guess...it could 1) search for words 2) if it is not found use some algorithm to try to "guess" the word. Could be something from AI like Hopfield network or back propagation network, or something else "identifying fingerprints", restoring broken data, or spelling corrections as Davide mentioned already...

Answer 3

Hmm... I thought that google used their vast corpus of data (the internet) to do some serious NLP (Natural Language Processing).

For example, they have so much data from the entire internet that they can count the number of times a three-word sequence occurs (known as a trigram). So if they see a sentence like: "pink frugr concert", they could see it has few hits, then find the most likely "pink * concert" in their corpus.

They apparently just do a variation of what Davide Gualano was saying, though, so definitely read that link. Google does of course use all web-pages it knows as a corpus, so that makes its algorithm particularly effective.

Answer 4

I saw something on this a few years back, so may have changed since, but apparently they started it by analysing their logs for the same users submitting very similar queries in a short space of time, and used machine learning based on how users had corrected themselves.

Answer 5

Simple. They have tons of data. They have statistics for every possible term, based on how often it is queried, and what variations of it usually yield results the users click... so, when they see you typed a frequent misspelling for a search term, they go ahead and propose the more usual answer.

Actually, if the misspelling is in effect the most frequent searched term, the algorythm will take it for the right one.

Answer 6

My guess is that they use a combination of a Levenshtein distance algorithm and the masses of data they collect regarding the searches that are run. They could pull a set of searches that have the shortest Levenshtein distance from the entered search string, then pick the one with the most results.

Answer 7

Here's the explanation directly from the source ( almost )

Search 101!

at min 22:03

Worth watching!

Basically and according to Douglas Merrill former CTO of Google it is like this:

1) You write a ( misspelled ) word in google

2) You don't find what you wanted ( don't click on any results )

3) You realize you misspelled the word so you rewrite the word in the search box.

4) You find what you want ( you click in the first links )

This pattern multiplied millions of times, shows what are the most common misspells and what are the most "common" corrections.

This way Google can almost instantaneously, offer spell correction in every language.

Also this means if overnight everyone start to spell night as "nigth" google would suggest that word instead.

EDIT

@ThomasRutter: Douglas describe it as "statistical machine learning".

They know who correct the query, because they know which query comes from which user ( using cookies )

If the users perform a query, and only 10% of the users click on a result and 90% goes back and type another query ( with the corrected word ) and this time that 90% clicks on a result, then they know they have found a correction.

They can also know if those are "related" queries of two different, because they have information of all the links they show.

Furthermore, they are now including the context into the spell check, so they can even suggest different word depending on the context.

See this demo of google wave ( @ 44m 06s ) that shows how the context is taken into account to automatically correct the spelling.

Here it is explained how that natural language processing works.

And finally here is an awesome demo of what can be done adding automatic machine translation ( @ 1h 12m 47s ) to the mix.

_{I've added anchors of minute and seconds to the videos to skip directly to the content, if they don't work, try reloading the page or scrolling by hand to the mark.}

Answer 8

For the theory of "did you mean" algorithm you can refer to Chapter 3 of Introduction to Information Retrieval. It is available online for free. Section 3.3 (page 52) exactly answers your question. And to specifically answer your update you only need a dictionary of words and nothing else (including millions of users).

Answer 9

Hi, regarding your question how to mimic the behavior without having tons of data - why not use tons of data collected by google? Download the google sarch results for the misspelled word and search for "Did you mean:" in the HTML.

I guess that's called mashup nowadays :-)

Answer 10

Easiest way to figure it out is to Google dynamic programming.

It's an algorithm that's been borrowed from Information Retrieval and is used heavily in modern day bioinformatics to see how similiar two gene sequences are.

Optimal solution uses dynamic programming and recursion.

This is a very solved problem with lots of solutions. Just google around until you find some open source code.

Answer 11

It should be obvious that they aren't simply doing spelling correction due to the various funny results that have popped up from time to time with real words. Things like:

"French military victories" -- no hits, did you mean "French military defeats"? That's humans using related words, not spelling.

Likewise "What to do if the grill gets wet" suggesting "What to do if the girl gets wet".

Answer 12

Google apparently suggests queries with best results, not with those which are spelled correctly. But in this case, probably a spell-corrector would be more feasible, Of course you could store some value for every query, based on some metric of how good results it returns.

So,

1. You need a dictionary (english or based on your data)

2. Generate a word trellis and calculate probabilities for the transitions using your dictionary.

3. Add a decoder to calculate minimum error distance using your trellis. Of course you should take care of insertions and deletions when calculating distances. Fun thing is that QWERTY keyboard maximizes the distance if you hit keys close to each other.(cae would turn car, cay would turn cat)

4. Return the word which has the minimum distance.

5. Then you could compare that to your query database and check if there is better results for other close matches.

Answer 13

This is quite interesting a post!

I'm writing a search engine but irrespective of algorithms used, there's a higher probability that the commonly misspeled words will always be misspelled by all generations as long as the language is the same.

It's interesting how few people have thought about the fact that some spelling mistakes are due to keyboard errors - Google ia also succeeding in trying to cover this using the concept of most common misspells and the most "common" corrections.

The future of search engines is flashing green!

God bless,

Chris Musasizi.

Have you read John 3:3, John 3:16?

Answer 14

Normally a production spelling corrector utilizes several methodologies to provide a spelling suggestion. Some are:

• Decide on a way to determine whether spelling correction is required. These may include insufficient results, results which are not specific or accurate enough (according to some measure), etc. Then:

• Use a large body of text or a dictionary, where all, or most are known to be correctly spelled. These are easily found online, in places such as LingPipe. Then to determine the best suggestion you look for a word which is the closest match based on several measures. The most intuitive one is similar characters. What has been shown through research and experimentation is that two or three character sequence matches work better. (bigrams and trigrams). To further improve results, weigh a higher score upon a match at the beginning, or end of the word. For performance reasons, index all these words as trigrams or bigrams, so that when you are performing a lookup, you convert to n-gram, and lookup via hashtable or trie.

• Use heuristics related to potential keyboard mistakes based on character location. So that "hwllo" should be "hello" because 'w' is close to 'e'.

• Use a phonetic key (Soundex, Metaphone) to index the words and lookup possible corrections. In practice this normally returns worse results than using n-gram indexing, as described above.

• In each case you must select the best correction from a list. This may be a distance metric such as levenshtein, the keyboard metric, etc.

• For a multi-word phrase, only one word may be misspelled, in which case you can use the remaining words as context in determining a best match.

Answer 15

There is a specific data structure - ternary search tree - that naturally supports partial matches and near-neighbor matches.

Answer 16

Try an image search of a person I used to know, "Robert Wiander". This ends up with "Did you mean 'Robert Winder'?" and I don't get ANY of the pictures on the person I was looking for.

Go even further, search for: "robert wiander" -winder and you get "Did you mean 'robert wander' -wonder?"

How does one turn this bastard of a feature OFF?!?

Answer 17

Use Levenshtein distance, then create a Metric Tree (or Slim tree) to index words. Then run a 1-Nearest Neighbour query, and you got the result.

Answer 18

Here is the VB.NET version of the Norvig Spelling Corrector. You may find this useful if it is not too late!