Levenshtein transducers accept a query term and return all terms in a dictionary that are within n spelling errors away from it. They constitute a highly-efficient (space and time) class of spelling correctors that work very well when you do not require context while making suggestions. Forget about performing a linear scan over your dictionary to find all terms that are sufficiently-close to the user's query, using a quadratic implementation of the Levenshtein distance or Damerau-Levenshtein distance, these babies find all the terms from your dictionary in linear time on the length of the query term (not on the size of the dictionary, on the length of the query term).
If you need context, then take the candidates generated by the transducer as a starting place, and plug them into whatever model you're using for context (such as by selecting the sequence of terms that have the greatest probability of appearing together).
For a quick demonstration, please visit the Github Page, here.
The library is currently written in Java, CoffeeScript, and JavaScript, but I will be porting it to other languages, soon. If you have a specific language you would like to see it in, or package-management system you would like it deployed to, let me know.
Install the module via npm:
% npm install liblevenshtein
info trying registry request attempt 1 at 12:59:16
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http 304 https://registry.npmjs.org/liblevenshtein
[email protected] node_modules/liblevenshtein
Then, you may require it to do whatever you need:
var levenshtein = require('liblevenshtein');
// Assume "completion_list" is a list of terms you want to match against in
// fuzzy queries.
var builder = new levenshtein.Builder()
.dictionary(completion_list, false) // generate spelling candidates from unsorted completion_list
.algorithm("transposition") // use Levenshtein distance extended with transposition
.sort_candidates(true) // sort the spelling candidates before returning them
.case_insensitive_sort(true) // ignore character-casing while sorting terms
.include_distance(false) // just return the ordered terms (drop the distances)
.maximum_candidates(10); // only want the top-10 candidates
// Maximum number of spelling errors we will allow the spelling candidates to
// have, with regard to the query term.
var MAX_EDIT_DISTANCE = 2;
var transducer = builder.build();
// Assume "term" corresponds to some query term. Once invoking
// transducer.transduce(term, MAX_EDIT_DISTANCE), candidates will contain a list
// of all spelling candidates from the completion list that are within
// MAX_EDIT_DISTANCE units of error from the query term.
var candidates = transducer.transduce(term, MAX_EDIT_DISTANCE);To use the library on your website, reference the desired file from the
<head/> of your document, like so:
<!DOCTYPE html>
<html>
<head>
<!-- stuff ... -->
<script type="text/javascript"
src="http://universal-automata.github.com/liblevenshtein/javascripts/2.0.4/levenshtein-transducer.min.js">
</script>
<!-- more stuff ... -->
</head>
<body>
<!-- yet another fancy document ... -->
</body>
</html>Once the script loads, you should construct a transducer via the Builder Api:
$(function ($) {
"use strict";
// Maximum number of spelling errors we will allow the spelling candidates to
// have, with regard to the query term.
var MAX_EDIT_DISTANCE = 2;
var completion_list = getCompletionList(); // fictitious method
var builder = new levenshtein.Builder()
.dictionary(completion_list, false) // generate spelling candidates from unsorted completion_list
.algorithm("transposition") // use Levenshtein distance extended with transposition
.sort_candidates(true) // sort the spelling candidates before returning them
.case_insensitive_sort(true) // ignore character-casing while sorting terms
.include_distance(false) // just return the ordered terms (drop the distances)
.maximum_candidates(10); // only want the top-10 candidates
var transducer = builder.build();
var $queryTerm = $('#query-term-input-field');
$queryTerm.keyup(function (event) {
var candidates, term = $.trim($queryTerm.val());
if (term) {
candidates = transducer.transduce(term, MAX_EDIT_DISTANCE);
printAutoComplete(candidates); // print the list of completions
} else {
clearAutoComplete(); // user has cleared the search box
}
return true;
});
});This will give the user autocompletion hints as he types in the search box.
This library is based largely on the work of Stoyan Mihov, Klaus Schulz, and Petar Nikolaev Mitankin: "Fast String Correction with Levenshtein-Automata". For more details, please see the wiki.