I have a regex created from a list in a database to match names for types of buildings in a game. The problem is typos, sometimes those writing instructions for their team in the game will misspell a building name and obviously the regex will then not pick it up (i.e. spelling "University" and "Unversity").
Are there any suggestions on making a regex match misspellings of 1 or 2 letters?
The regex is dynamically generated and run on a local machine that's able to handle a lot more load so I have as a last resort to algorithmically create versions of each word with a letter missing and then another with letters added in.
I'm using PHP but I'd hope that any solution to this issue would not be PHP specific.
Allow me to introduce you to the Levenshtein Distance, a measure of the difference between strings as the number of transformations needed to convert one string to the other.
It's also built into PHP.
So, I'd split the input file by non-word characters, and measure the distance between each word and your target list of buildings. If the distance is below some threshold, assume it was a misspelling.
I think you'd have more luck matching this way than trying to craft regex's for each special case.
Google's implementation of "did you mean" by looking at previous results might also help:
How do you implement a "Did you mean"?
What is Soundex() ? – Teifion (28 mins ago)
A soundex is similar to the levenshtein function Triptych mentions. It is a means of comparing strings. See: http://us3.php.net/soundex
You could also look at metaphone and similar_text. I would have put this in a comment but I don't have enough rep yet to do that. :D
Back in the days we sometimes used Soundex() for these problems.
You're in luck; the algorithms folks have done lots of work on approximate matching of regular expressions. The oldest of these tools is probably agrep originally developed at the University of Arizona and now available in a nice open-source version. You simply tell agrep how many mistakes you are willing to tolerate and it matches from there. It can also match other blocks of text besides lines. The link above has links to a newer, GPLed version of agrep and also a number of language-specific libraries for approximate matching of regular expressions.
This might be overkill, but Peter Norvig of Google has written an excellent article on writing a spell checker in Python. It's definitely worth a read and might apply to your case.
At the end of the article, he's also listed contributed implementations of the algorithm in various other languages.
Related
I'm not sure if this is possible, but is there a way (pre-written library or known scientific detection scheme) to analyse a few sentences of text and determine if the sentences rhyme? A colleague suggested comparing the first and last word and using a thesaurus, but I don't quite understand how that would work.
High accuracy is not what I am aiming for, an accuracy of even 20% would be awesome, it's for a gimmicky little web application idea I have nothing important just thought it would be cool.
I am open to trying other languages, perhaps even Python which I've heard is great for analysing text but PHP would be preferable.
Metaphone http://www.php.net/manual/en/function.metaphone.php
You could classify an input into phonetics (sounds) and then check if the same sound appears frequently. Since each one should match up with syllables, you could calculate the Levenshtein distance (count the syllables between the matches) to see if they fit into some known pattern, I.e. haiku.
http://www.php.net/manual/en/function.levenshtein.php
http://php.net/manual/en/function.soundex.php
This is something I'm working on and I'd like input from the intelligent people here on StackOverflow.
What I'm attempting is a function to repair text based on combining various bad versions of the same text page. Basically this can be used to combine different OCR results into one with greater accuracy than any of them individually.
I start with a dictionary of 600,000 English words, that's pretty much everything including legal and medical terms and common names. I have this already.
Then I have 4 versions of the text sample.
Something like this:
$text[0] = 'Fir5t text sample is thisline';
$text[1] = 'Fir5t text Smplee is this line.';
$text[2] = 'First te*t sample i this l1ne.';
$text[3] = 'F i r st text s ample is this line.';
I attempting to combine the above to get an output which looks like:
$text = 'First text sample is this line.';
Don't tell me it's impossible, because it is certainly not, just very difficult.
I would very much appreciate any ideas anyone has towards this.
Thank you!
My current thoughts:
Just checking the words against the dictionary will not work, since some of the spaces are in the wrong place and occasionally the word will not be in the dictionary.
The major concern is repairing broken spacings, once this is fixed then then the most commonly occurring dictionary word can be chosen if exists, or else the most commonly occurring non-dictionary word.
Have you tried using a longest common subsequence algorithm? These are commonly seen in the "diff" text comparison tools used in source control apps and some text editors. A diff algorithm helps identify changed and unchanged characters in two text samples.
http://en.wikipedia.org/wiki/Diff
Some years ago I worked on an OCR app similar to yours. Rather than applying multiple OCR engines to one image, I used one OCR engine to analyze multiple versions of the same image. Each of the processed images was the result of applying different denoising technique to the original image: one technique worked better for low contrast, another technique worked better when the characters were poorly formed. A "voting" scheme that compared OCR results on each image improved the read rate for arbitrary strings of text such as "BQCM10032". Other voting schemes are described in the academic literature for OCR.
On occasion you may need to match a word for which no combination of OCR results will yield all the letters. For example, a middle letter may be missing, as in either "w rd" or "c tch" (likely "word" and "catch"). In this case it can help to access your dictionary with any of three keys: initial letters, middle letters, and final letters (or letter combinations). Each key is associated with a list of words sorted by frequency of occurrence in the language. (I used this sort of multi-key lookup to improve the speed of a crossword generation app; there may well be better methods out there, but this one is easy to implement.)
To save on memory, you could apply the multi-key method only to the first few thousand common words in the language, and then have only one lookup technique for less common words.
There are several online lists of word frequency.
http://en.wiktionary.org/wiki/Wiktionary:Frequency_lists
If you want to get fancy, you can also rely on prior frequency of occurrence in the text. For example, if "Byrd" appears multiple times, then it may be the better choice if the OCR engine(s) reports either "bird" or "bard" with a low confidence score. You might load a medical dictionary into memory only if there is a statistically unlikely occurrence of medical terms on the same page--otherwise leave medical terms out of your working dictionary, or at least assign them reasonable likelihoods. "Prosthetics" is a common word; "prostatitis" less so.
If you have experience with image processing techniques such as denoising and morphological operations, you can also try preprocessing the image before passing it to the OCR engine(s). Image processing could also be applied to select areas after your software identifies the words or regions where the OCR engine(s) fared poorly.
Certain letter/letter and letter/numeral substitutions are common. The numeral 0 (zero) can be confused with the letter O, C for O, 8 for B, E for F, P for R, and so on. If a word is found with low confidence, or if there are two common words that could match an incompletely read word, then ad hoc shape-matching rules could help. For example, "bcth" could match either "both" or "bath", but for many fonts (and contexts) "both" is the more likely match since "o" is more similar to "c" in shape. In a long string of words such as a a paragraph from a novel or magazine article, "bath" is a better match than "b8th."
Finally, you could probably write a plugin or script to pass the results into a spellcheck engine that checks for noun-verb agreement and other grammar checks. This may catch a few additional errors. Maybe you could try VBA for Word or whatever other script/app combo is popular these days.
Tackling complex algorithms like this by yourself will probably take longer and be more error prone than using a third party tool - unless you really need to program this yourself, you can check the Yahoo Spelling Suggestion API. They allow 5.000 requests per IP per day, I believe.
Others may offer something similar (I think there's a bing API, too).
UPDATE: Sorry, I just read that they've stopped this service in April 2011. They claim to offer a similar service called "Spelling Suggestion YQL table" now.
This is indeed a rather complicated problem.
When I do wonder how to spell a word, the direct way is to open a dictionary. But what if it is a small complex sentence that I'm trying to spell correctly ? One of my personal trick, which works most of the time, is to call Google. I place my sentence between quotes on Google and count the results. Here is an example : entering "your very smart" on Google gives 13'600k page. Entering "you're very smart" gives 20'000k pages. Then, likely, the correct spelling is "you're very smart". And... indeed it is ;)
Based on this concept, I guess you have samples which, for the most parts, are correctly misspelled (well, maybe not if your develop for a teens gaming site...). Can you try to divide the samples into sub pieces, not going up to the words, and matching these by frequency ? The most frequent piece is the most likely correctly spelled. Prior to this, you can already make a dictionary spellcheck with your 600'000 terms to increase the chance that small spelling mistakes will alredy be corrected. This should increase the frequency of correct sub pieces.
Dividing the sentences in pieces and finding the right "piece-size" is also tricky.
What concerns me a little too : how do you extract the samples and match them together to know the correctly spelled sentence is the same (or very close?). Your question seems to assume you have this, which also seems something very complex for me.
Well, what precedes is just a general tip based on my personal and human experience. Donno if this can help. This is obviously not a real answer and is not meant to be one.
You could try using google n-grams to achieve this.
If you need to get right string only by comparing other. Then Something like this maybe will help.
It not finished yet, but already gives some results.
$text[0] = 'Fir5t text sample is thisline';
$text[1] = 'Fir5t text Smplee is this line.';
$text[2] = 'First te*t sample i this l1ne.';
$text[3] = 'F i r st text s ample is this line.';
function getRight($arr){
$_final='';
$count=count($arr);
// Remove multi spaces AND get string lengths
for($i=0;$i<$count;$i++){
$arr[$i]=preg_replace('/\s\s+/', ' ',$arr[$i]);
$len[$i]=strlen($arr[$i]);
}
// Max length
$_max=max($len);
for($i=0;$i<$_max;$i++){
$_el=array();
for($j=0;$j<$count;$j++){
// Cheking letter counts
$_letter=$arr[$j][$i];
if(isset($_el[$_letter]))$_el[$_letter]++;
else$_el[$_letter]=1;
}
//Most probably count
list($mostProbably) = array_keys($_el, max($_el));
$_final.=$mostProbably;
// If probbaly example is not space
if($_el!=' '){
// THERE NEED TO BE CODE FOR REMOVING SPACE FROM LINES WHERE $text[$i] is space
}
}
return $_final;
}
echo getRight($text);
I have this problem to solve in my PHP project where some keywords (from a few hundreds to a few thousands, lengths can vary) need to be searched in a string about 100-300 characters long, sometimes of lesser length 30-50 chars. I can preprocess the keywords for reusing for new instances of search strings. I am kind of new to PHP and did not find a way to do this in the PHP library. Doing a bit of searching, I found a few good candidates in Aho Corasick algorithm and then this improvement by Sun Wu and Udi Manber, which also seems to be known as agrep (or is a part of agrep): http://webglimpse.net/pubs/TR94-17.pdf
There is Rabin Karp, Suffix Trees etc too but they did not look quite suitable as first was for fixed length keywords and latter seems quite generic and will need rather a lot of work.
Can anyone let me know if implementing the Agrep/Sun Wu-Manber on my own in php is a good way to solve this problem? Any other feedback?
EDIT: as I mentioned below in a comment, there are hundreds or more of distinct search keywords, so regex will not help. So that response is not helpful.
I think you can solve this problem by using "Levenshtein distance" metric.
From wikipedia;
In information theory and computer science, the Levenshtein distance
is a string metric for measuring the amount of difference between two
sequences.
Plus, PHP has a levenshtein() method. Use your keyword list as array & searchable string as input and iterate over your array and use levenshtein() in each iteration for matching.
As of PHP 5.5, PHP's strtr uses the Wu-Manbers algorithm for multi-pattern matching. See commit ccf15cf2 in the PHP git repository for details about the implementation. It is quite efficient, in my experience.
A pure-PHP implementation of the Aho-Corasick algorithm is available here: https://packagist.org/packages/wikimedia/aho-corasick
I have a particular problem and need to know the best way to go about solving it.
I have a php string that can contain a number of keywords (tags actually). For example:-
"seo, adwords, google"
or
"web development, community building, web design"
I want to create a pool of keywords that are related, so all seo, online marketing related keywords or all web development related keywords.
I want to check the keyword / tag string against these pools of keywords and if for example seo or adwords is contained within the keyword string it is matched against the keyword pool for online marketing and a particular piece of content is served.
I wish to know the best way of coding this. I'm guessing some kind of hash table or array but not sure the best way to approach it.
Any ideas?
Thanks
Jonathan
Three approaches come to my mind, although I'm sure there could be more. Of course in any case I would store the values in a database table (or config file, or whatever depending on your application) so it can be edited easily.
1) Easiest: Convert the list into a regular expression of the form "keyword1|keyword2|keyword3" and see if the input matches.
2) Medium: Add the words to a hashtable, then split the input into words (you may have to use regular expression replacing to remove punctuation) and try to find each word of input in the hashtable.
3) Hardest: This may not work depending on your exact situation, but if all the possible content can be indexed by a search solution (like Apache SOLR, for example) then your list of keywords could be used as a search string and you could return results above a particular level of relevance.
It's hard to know exactly which solution would work best without knowing more about your source data. A large number of keywords may jam up a regular expression, but if it's a short list then it might work great. If your inputs are long then #2 won't work so well because you have to test each and every input word. As always your mileage may vary, so I would start with the easiest solution I thought would work and see if the performance is acceptable.
I am having a hard time to find a way to detect if two words has the same rhyme in English. It has not to be the same syllabic ending but something closer to phonetically similarity.
I can not believe in 2009 the only way of doing it is using those old fashioned rhyme dictionaries. Do you know any resources (in PHP would be a plus) to help me in this painful task?
Thank you.
Your hints were all really hepful. I will take some time to investigate it. Anyway, more info about DoubleMetaPhone can be found here in a proper PHP code (the other one is an extension).
There are interesting information about MethaPhone function and doublemetaphone in Php.net.
They specially alert about how slow double metaphone is compared with metaphone (something like 100 times slower).
Soundex won't help you. Soundex focuses on the beginning of the word, not its ending. Generally it think you'll have hard time finding any tool to do this. Even to the linguist the root of the word is more interesting, than it's ending.
Generally what you'll have to do is to divide words in syllables and compare their last syllable. Even better if you could divide it in phonemes, reverse their order and do comparison on reversed word. You might trying comparing last part of metaphone keys.
See Bradley Buda's CS project summary from U. Michigan, which uses Levenshtein distance as an atom in finding rhyming English words. I believe combining Levenshtein and soundex should give better results.
Besides the soundex() function ramonzoellner mentioned, there is another function called levenshtein() which calculates the levenshtein distance between the two words. That may help you further.
Seems like you need to find a database containing pronunciation, and possibly stress/emphasis: multisyllabic words with similar last syllables, but stresses on different syllables don't quite rhyme, at least in the sense of being able to use them in poems; e.g. "poems" and "hems". The other answers (levenshtein & soundex) should help for locating candidates, but they won't confirm it:
tough
cough
dough
through
bough
Did you try the soundex() function? It should give you at least some indication if the words sound alike.