So, suppose I have a simple array of sentences. What would be the best way to search it based on user input, and return the closest match?
The Levenshtein functions seem promising, but I don't think I want to use them. User input may be as simple as highest mountain, in which case I'd want to search for the sentence in the array that has highest mountain. If that exact phrase does not exist, then I'd want to search for the sentence that has highest AND mountain, but not back-to-back, and so on. The Levenshtein functions work on a per-character basis, but what I really need is a per-word basis.
Of course, to some degree, Levenshtein functions may still be useful, as I'd also want to take into account the possibility of the sentence containing the phrase highest mountains (notice the S) or similar.
What do you suggest? Are there any systems for PHP that do this that already exist? Would Levenshtein functions alone be an adequate solution? Is there a word-based Levenshtein function that I don't know about?
Thanks!
EDIT - I have considered both MySQL fulltext search, and have also considered the possibility of breaking both A) input and B) each sentence into separate arrays of words, and then compared that way, using Levenshtein functions to account for variations in words. (color, colour, colors, etc) However, I am concerned that this method, though possibly clever, may be computationally taxing.
As I am not a fan of writing code for you, I would normally ask you what you have tried first. However, I was currently stuck on something, so took a break to write this:
$results=array();
foreach($array as $sentence){
if(stripos($sentence,$searchterm)!==false)
$results[]=$sentence;
}
if(count($results)==0){
$wordlist=explode(" ",$searchterm);
foreach($wordlist as $word){
foreach($array as $sentence){
if(stripos($sentence,$word)!==false)
$results[]=$sentence;
}
}
}
print_r($results);
This will search an array of sentences for terms exactly. It will not find a result if you typed in "microsift" and the sentence had the word "Microsoft". It is case insensitive, so it should work better. If no results are found using the full term, it is broken up and searched by word. Hope this at least points you to a starting place.
Check this: http://framework.zend.com/manual/en/zend.search.lucene.overview.html
Zend_Search_Lucene offers a HTML parsing feature. Documents can be created directly from a HTML file or string:
$doc = Zend_Search_Lucene_Document_Html::loadHTML($htmlString);
$index->addDocument($doc);
There are not built-in functions for PHP to do this. This is because what you are asking for involves search relevance, related terms, iterative searching, and many more complex operations that need to mimic human logic in searching. You can try looking for PHP-based search classes, although the ones that I know are database search engines rather than array search classes. Making your own is prohibitively complex.
Related
Im pulling in data from a 6 live feeds which is sometimes have slightly different formatting, ie. i might have
'arsenal' and 'arsenal fc'
'T Walcot' and 'Theo Walcot' and 'T. Walcot'
What i was wandering was, is there a simple way to check if the strings match each other on the basis of if they have a certain % of letters in the same order they would be considered the same.
I susppose i could setup a list of related words and terms, but this would mean having to setup it up in advance, but i was wandering if there was an easier, on the fly automated way as i wont be able to compile a full list for a long time.
There's a function just for that:
similar_text('Theo Walcott', 'T. Walcott', $similarity);
echo $similarity;
Have a look at the soundex function http://php.net/soundex and the similar_text function to get a percentage of similarity.
My PHP script needs to check for matches throughout an array of data. It's currently looking for exact string matches. I'd like it to be less strict.
For example, if the array holds the string "Tom and Jerry" I would like to return true for: "Tom Jerry", "Tom & Jerry" and maybe even "Tom and Jery". I found links to PHP search engines they are more complex and not really what I need. My data is fairly small and dynamic, so there's no indexing.
I know I could write a big hairy regular expression, but I'm pretty sure I would be reinventing the wheel, because I'm sure others have already done this. Any advice on where to look or how to approach this would be much appreciated.
EDIT: To clarify, I'm trying to avoid entering all the dynamically generated data into a DB.
If the data were in MySQL, you could use a full text search. This is quite easy to develop; the question is: would that be too heavy-weight of a solution?
It may require some trial and error but you could do:
Make a manual list of words that may be absent, such 'and', 'in', 'of', et cetera (such as in your Tom Jerry example).
Compute the Hamming distance between the string and the search query. If it is low (perhaps at most one or two), return true.
Otherwise, return false.
I just discovered two functions which appear to do what I want:
similar_text()
levenshtein()
Both seem to return an intiger representing the "closeness" of the match between two strings. The difference between the two is over my head.
My search was aided by this S.O. question.
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 am doing an experimental project.
What i am trying to achieve is, i want to find that what are the keywords in that text.
How i am trying to do this is i make a list of how many times a word appear in the text sorted by most used words at top.
But problem is some common words like is,was,were are always at top. Apparently these are not worth.
Can you people suggest me some good logic to do it, so it finds good related keywords always?
Use something like a Brill Parser to identify the different parts of speech, like nouns. Then extract only the nouns, and sort them by frequency.
Well you could use preg_split to get the list of words and how often they occur, I'm assuming that that's the bit you've got working so far.
Only thing I could think of regarding stripping the non-important words is to have a dictionary of words you want to ignore, containing "a", "I", "the", "and", etc. Use this dictionary to filter out the unwanted words.
Why are you doing this, is it for searching page content? If it is, then most back end databases offer some kind of text search functionality, both MySQL and Postgres have a fulltext search engine, for example, that automatically discards the unimportant words. I'd recommend using the fulltext features of the backend database you're using, as chances are they're already implementing something that meets your requirements.
my first approach to something like this would be more mathematical modeling than pure programming.
there are two "simple" ways you can attack a problem like this;
a) exclusion list (penalize a collection of words which you deem useless)
b) use a weight function, which for ex. builds on the word length, thus small words such as prepositions (in, at...) and pronouns (I,you,me,his... ) will be penalized and hopefully fall mid-table
I am not sure if this was what you were looking for, but I hope it helps.
By the way, I know that contextual text processing is a subject of active research, you might find a number of projects which may be interesting.
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.