I have two subtitles files.
I need a function that tells whether they represent the same text, or the similar text
Sometimes there are comments like "The wind is blowing... the music is playing" in one file only.
But 80% percent of the contents will be the same. The function must return TRUE (files represent the same text).
And sometimes there are misspellings like 1 instead of l (one - L ) as here:
She 1eft the baggage.
Of course, it means function must return TRUE.
My comments:
The function should return percentage of the similarity of texts - AGREE
"all the people were happy" and "all the people were not happy" - here that'd be considered as a misspelling, so that'd be considered the same text. To be exact, the percentage the function returns will be lower, but high enough to say the phrases are similar
Do consider whether you want to apply Levenshtein on a whole file or just a search string - not sure about Levenshtein, but the algorithm must be applied to the file as a whole. It'll be a very long string, though.
Levenshtein algorithm: http://en.wikipedia.org/wiki/Levenshtein_distance
Anything other than a result of zero means the text are not "identical". "Similar" is a measure of how far/near they are. Result is an integer.
For the problem you've described (i.e. compering large strings), you can use Cosine Similarity, which return a number between 0 (completely different) to 1 (identical), base on the term frequency vectors.
You might want to look at several implementations that are described here: Cosine Similarity
You're expecting too much here, it looks like you would have to write a function for your specific needs. I would recommend starting with an existing file comparison application (maybe diff already has everything you need) and improve it to provide good results for your input.
Have a look at approximate grep. It might give you pointers, though it's almost certain to perform abysmally on large chunks of text like you're talking about.
EDIT: The original version of agrep isn't open source, so you might get links to OSS versions from http://en.wikipedia.org/wiki/Agrep
There are many alternatives to the Levenshtein distance. For example the Jaro-Winkler distance.
The choice for such algorithm is depending on the language, type of words, are the words entered by human and many more...
Here you find a helpful implementation of several algorithms within one library
if you are still looking for the solution then go with S-Bert (Sentence Bert) which is light weight algorithm which internally uses cosine similarly.
Related
I'm working on implementing probablistic matching for person record searching. As part of this, I plan to have blocking performed before any scoring is done. Currently, there are a lot of good options for transforming strings so that they can be stored and then searched for, with similar strings matching each other (things like soundex, metaphone, etc).
However, I've struggled to find something similar for purely numeric values. For example, it would be nice to be able to block on a social security number and not have numbers that are off or have transposed digits be removed from the results. 123456789 should have blocking results for 123456780 or 213456789.
Now, there are certainly ways to simply compare two numerical values to determine how similar they are, but what could I do when there are million of numbers in the database? It's obviously impractical to compare them all (and that would certainly invalidate the point of blocking).
What would be nice would be something where those three SSNs above could somehow be transformed into some other value that would be stored. Purely for example, imagine those three numbers ended up as AAABBCCC after this magical transformation. However, something like 987654321 would be ZZZYYYYXX and 123547698 would be AAABCCBC or something like that.
So, my question is, is there a good transformation for numeric values like there exists for alphabetical values? Or, is there some other approach that might make sense (besides some highly complex or low performing SQL or logic)?
The first thing to realize is that social security numbers are basically strings of digits. You really want to treat them like you would strings rather than numbers.
The second thing to realize is that your blocking function maps from a record to a list of strings that identify comparison worthy sets of items.
Here is some Python code to get you started. (I know you asked for Java, but I think the Python is clear and you aren't paying me enough to write it in Java :P ). The basic idea is to take your input record, simulate roughing it up in multiple ways (to get your blocking keys), and then group on by any match on those blocking keys.
import itertools
def transpositions(s):
for pos in range(len(s) - 1):
yield s[:pos] + s[pos + 1] + s[pos] + s[pos + 2:]
def substitutions(s):
for pos in range(len(s)):
yield s[:pos] + '*' + s[pos+1:]
def all_blocks(s):
return itertools.chain([s], transpositions(s), substitutions(s))
def are_blocked_candidates(s1, s2):
return bool(set(all_blocks(s1)) & set(all_blocks(s2)))
assert not are_blocked_candidates('1234', '5555')
assert are_blocked_candidates('1234', '1239')
assert are_blocked_candidates('1234', '2134')
assert not are_blocked_candidates('1234', '1255')
I'm making a chat responder for a game and i want know if there is a way you can compare two strings and see if they are approximatley equal to each other for example:
if someone typed:
"Strength level?"
it would do a function..
then if someone else typed:
"Str level?"
it would do that same function, but i want it so that if someone made a typo or something like that it would automatically detect what they're trying to type for example:
"Strength tlevel?"
would also make the function get called.
is what I'm asking here something simple or will it require me to make a big giant irritating function to check the Strings?
if you've been baffled by my explanation (Not really one of my strong points) then this is basically what I'm asking.
How can I check if two strings are similar to each other?
See this question and answer: Getting the closest string match
Using some heuristics and the Levenshtein distance algorithm, you can compute the similarity of two strings and take a guess at whether they're equal.
Your only option other than that would be a dictionary of accepted words similar to the one you're looking for.
You can use Levenshtein distance.
I believe you should use one of Edit distance algorithms to solve your problem. Here is for example Levenstein distance algorithm implementation in java. You may use it to compare words in the sentences and if sum of their edit distances would be less than for example 10% of sentence length consider them equals.
Perhaps what you need is a large dictionary for similar words and common spelling mistakes, for which you would use for each word to "translate" to one single entry or key.
This would be useful for custom words, so you could add "str" in the same key as "strength".
However, you could also make a few automated methods, i.e. when your word isn't found in the dictionary, to loop recursively for 1 letter difference (either missing or replaced) and can recurse into deeper levels, i.e. 2 missing letters etc.
I found a few projects that do text to phonemes translations, don't know which one is best
http://mary.dfki.de/
http://www2.eng.cam.ac.uk/~tpl/asp/source/Phoneme.java
http://java.dzone.com/announcements/announcing-phonemic-10
If you want to find similar word beginnings, you can use a stemmer. Stemmers reduce words to a common beginning. The most known algorithm if the Port Stemmer (http://tartarus.org/~martin/PorterStemmer).
Levenshtein, as pointed above, is great, but computational heavy for distances greater than one or two.
What would be the best approach to compare two hexadecimal file signatures against each other for similarities.
More specifically, what I would like to do is to take the hexadecimal representation of an .exe file and compare it against a series of virus signature. For this approach I plan to break the file (exe) hex representation into individual groups of N chars (ie. 10 hex chars) and do the same with the virus signature. I am aiming to perform some sort of heuristics and therefore statistically check whether this exe file has X% of similarity against the known virus signature.
The simplest and likely very wrong way I thought of doing this is, to compare exe[n, n-1] against virus [n, n-1] where each element in the array is a sub array, and therefore exe1[0,9] against virus1[0,9]. Each subset will be graded statistically.
As you can realize there would be a massive number of comparisons and hence very very slow. So I thought to ask whether you guys can think of a better approach to do such comparison, for example implementing different data structures together.
This is for a project am doing for my BSc where am trying to develop an algorithm to detect polymorphic malware, this is only one part of the whole system, where the other is based on genetic algorithms to evolve the static virus signature. Any advice, comments, or general information such as resources are very welcome.
Definition: Polymorphic malware (virus, worm, ...) maintains the same functionality and payload as their "original" version, while having apparently different structures (variants). They achieve that by code obfuscation and thus altering their hex signature. Some of the techniques used for polymorphism are; format alteration (insert remove blanks), variable renaming, statement rearrangement, junk code addition, statement replacement (x=1 changes to x=y/5 where y=5), swapping of control statements. So much like the flu virus mutates and therefore vaccination is not effective, polymorphic malware mutates to avoid detection.
Update: After the advise you guys gave me in regards what reading to do; I did that, but it somewhat confused me more. I found several distance algorithms that can apply to my problem, such as;
Longest common subsequence
Levenshtein algorithm
Needleman–Wunsch algorithm
Smith–Waterman algorithm
Boyer Moore algorithm
Aho Corasick algorithm
But now I don't know which to use, they all seem to do he same thing in different ways. I will continue to do research so that I can understand each one better; but in the mean time could you give me your opinion on which might be more suitable so that I can give it priority during my research and to study it deeper.
Update 2: I ended up using an amalgamation of the LCSubsequence, LCSubstring and Levenshtein Distance. Thank you all for the suggestions.
There is a copy of the finished paper on GitHub
For algorithms like these I suggest you look into the bioinformatics area. There is a similar problem setting there in that you have large files (genome sequences) in which you are looking for certain signatures (genes, special well-known short base sequences, etc.).
Also for considering polymorphic malware, this sector should offer you a lot, because in biology it seems similarly difficult to get exact matches. (Unfortunately, I am not aware of appropriate approximative searching/matching algorithms to point you to.)
One example from this direction would be to adapt something like the Aho Corasick algorithm in order to search for several malware signatures at the same time.
Similarly, algorithms like the Boyer Moore algorithm give you fantastic search runtimes especially for longer sequences (average case of O(N/M) for a text of size N in which you look for a pattern of size M, i.e. sublinear search times).
A number of papers have been published on finding near duplicate documents in a large corpus of documents in the context of websearch. I think you will find them useful. For example, see
this presentation.
There has been a serious amount of research recently into automating the detection of duplicate bug reports in bug repositories. This is essentially the same problem you are facing. The difference is that you are using binary data. They are similar problems because you will be looking for strings that have the same basic pattern, even though the patterns may have some slight differences. A straight-up distance algorithm probably won't serve you well here.
This paper gives a good summary of the problem as well as some approaches in its citations that have been tried.
ftp://ftp.computer.org/press/outgoing/proceedings/Patrick/apsec10/data/4266a366.pdf
As somebody has pointed out, similarity with known string and bioinformatics problem might help. Longest common substring is very brittle, meaning that one difference can halve the length of such a string. You need a form of string alignment, but more efficient than Smith-Waterman. I would try and look at programs such as BLAST, BLAT or MUMMER3 to see if they can fit your needs. Remember that the default parameters, for these programs, are based on a biology application (how much to penalize an insertion or a substitution for instance), so you should probably look at re-estimating parameters based on your application domain, possibly based on a training set. This is a known problem because even in biology different applications require different parameters (based, for instance, on the evolutionary distance of two genomes to compare). It is also possible, though, that even at default one of these algorithms might produce usable results. Best of all would be to have a generative model of how viruses change and that could guide you in an optimal choice for a distance and comparison algorithm.
I have a list of people that I'd like to search through. I need to know 'how much' each item matches the string it is being tested against.
The list is rather small, currently 100+ names, and it probably won't reach 1000 anytime soon.
Therefore I assumed it would be OK to keep the whole list in memory and do the searching using something Java offers out-of-the-box or using some tiny library that just implements one or two testing algorithms. (In other words without bringing-in any complicated/overkill solution that stores indexes or relies on a database.)
What would be your choice in such case please?
EDIT: Seems like Levenshtein has closest to what I need from what has been adviced. Only that gets easily fooled when the search query is "John" and the names in list are significantly longer.
You should look at various string comparison algorithms and see which one suits your data best. Options are Jaro-Winkler, Smith-Waterman etc. Look up SimMetrics - a F/OSS library that offers a very comprehensive set of string comparison algorithms.
If you are looking for a 'how much' match, you should use Soundex. Here is a Java implementation of this algorithm.
Check out Double Metaphone, an improved soundex from 1990.
http://commons.apache.org/codec/userguide.html
http://svn.apache.org/viewvc/commons/proper/codec/trunk/src/java/org/apache/commons/codec/language/DoubleMetaphone.java?view=markup
According to me Jaro-Winkler algorithm will suit your requirement best.
Here is a Short summary of Jaro-Winkler Distance Algo
One of the PDF which compares different algorithms --> Link to PDF
is there a dictionary i can download for java?
i want to have a program that takes a few random letters and sees if they can be rearanged into a real word by checking them against the dictionary
Is there a dictionary i can download
for java?
Others have already answered this... Maybe you weren't simply talking about a dictionary file but about a spellchecker?
I want to have a program that takes a
few random letters and sees if they
can be rearranged into a real word by
checking them against the dictionary
That is different. How fast do you want this to be? How many words in the dictionary and how many words, up to which length, do you want to check?
In case you want a spellchecker (which is not entirely clear from your question), Jazzy is a spellchecker for Java that has links to a lot of dictionaries. It's not bad but the various implementation are horribly inefficient (it's ok for small dictionaries, but it's an amazing waste when you have several hundred thousands of words).
Now if you just want to solve the specific problem you describe, you can:
parse the dictionary file and create a map : (letters in sorted order, set of matching words)
then for any number of random letters: sort them, see if you have an entry in the map (if you do the entry's value contains all the words that you can do with these letters).
abracadabra : (aaaaabbcdrr, (abracadabra))
carthorse : (acehorrst, (carthorse) )
orchestra : (acehorrst, (carthorse,orchestra) )
etc...
Now you take, say, three random letters and get "hsotrerca", you sort them to get "acehorrst" and using that as a key you get all the (valid) anagrams...
This works because what you described is a special (easy) case: all you need is sort your letters and then use an O(1) map lookup.
To come with more complicated spell checkings, where there may be errors, then you need something to come up with "candidates" (words that may be correct but mispelled) [like, say, using the soundex, metaphone or double metaphone algos] and then use things like the Levenhstein Edit-distance algorithm to check candidates versus known good words (or the much more complicated tree made of Levenhstein Edit-distance that Google use for its "find as you type"):
http://en.wikipedia.org/wiki/Levenshtein_distance
As a funny sidenote, optimized dictionary representation can store hundreds and even millions of words in less than 10 bit per word (yup, you've read correctly: less than 10 bits per word) and yet allow very fast lookup.
Dictionaries are usually programming language agnostic. If you try to google it without using the keyword "java", you may get better results. E.g. free dictionary download gives under each dicts.info.
OpenOffice dictionaries are easy to parse line-by-line.
You can read it in memory (remember it's a lot of memory):
List words = IOUtils.readLines(new FileInputStream("dicfile.txt")) (from commons-io)
Thus you get a List of all words. Alternatively you can use the Line Iterator, if you encounter memory prpoblems.
If you are on a unix like OS look in /usr/share/dict.
Here's one:
http://java.sun.com/docs/books/tutorial/collections/interfaces/examples/dictionary.txt
You can use the standard Java file handling to read the word on each line:
http://www.java-tips.org/java-se-tips/java.io/how-to-read-file-in-java.html
Check out - http://sourceforge.net/projects/test-dictionary/, it might give you some clue
I am not sure if there are any such libraries available for download! But I guess you can definitely digg through sourceforge.net to see if there are any or how people have used dictionaries - http://sourceforge.net/search/?type_of_search=soft&words=java+dictionary