Just a thought … if Levenshtein would anyway account for edit distances <=2, then there is no need to call the edits1( ) function anymore.

ok … Implemented LD, there is tremendous improvement in performance. Thanks for the pointers Dante.
I have re-uploaded the new source. I have retained both approaches for comparison.

Why not try Levenshtein Distance Algorithm

for each word in corpus
if(len[word]-len[input] is not between -2 and 2)
continue;
find edit distance
if(edit distance > 2)
continue;
result[word]=1
output word with max probability

Yes, (2) is an optimization that should be done. i will change the code and re-upload.
(3) is a good idea. Perhaps train method can be executed on the server side and the pre-processed corpus can be used on the client (browser).

Regarding (1), though it is true that calling wordexists on the result set of edits1 will improve the performance, I am not sure, if it will eliminate certain possible states from the overall results. Is there a way to ascertain that the reduced result set of edits1 would still provide the same set that Known_edits2() does when edits1 is not reduced (using wordexists). Maybe I will test it with brown corpus and analyze the impact of a reduced edits1.

Also, thanks for the pre-processed brown corpus file.

1)Wordexists function should be applied to resultset before

returning the resultset in edits1.Thats most probably affecting

the performance of edits2.

2)Rewriting train(features)
for(f in features)
{
if(model[features[f]])
model[features[f]]+=1;
else
model[features[f]]=1;
}

3)I dont think its a particularly good idea to import the corpus

as I hope this is a part of web application. The simplest

brown corpus is about 7 MB. Instead you can import the

words and their frequency that have been already

preprocessed. Here is the frequency list from brown corpus

that I have developed for my other projects

http://www.mediafire.com/?sharekey=5cacc0942f2ed052d2db6fb9a8902bda