I have a set of pairs of character strings, e.g.:
abba - aba,
haha - aha,
baa - ba,
exb - esp,
xa - za
The second (right) string in the pair is somewhat similar to the first (left) string.
That is, a character from the first string can be represented by nothing, itself or a character from a small set of characters.
There's no simple rule for this character-to-character mapping, although there are some patterns.
Given several thousands of such string pairs, how do I deduce the transformation rules such that if I apply them to the left strings, I get the right strings?
The solution can be approximate, working correctly for, say, 80-95% of the strings.
Would you recommend to use some kind of a genetic algorithm? If so, how?
If you could align the characters, or rather groups of characters, you could work out tables saying that aa => a, bb => z, and so on. If you had such tables, you could align the characters using http://en.wikipedia.org/wiki/Dynamic_time_warping. One approach is therefore to guess an alignment (e.g. one for one, just as a starting point, or just align the first and last characters of each sequence), work out a translation table from that, use DTW to get a new alignment, work out a revised translation table, and iterate in that way. Perhaps you could wrap this up with enough maths to show that there is some measure of optimality or probability that such passes increase, climbing to a local maximum.
There is probably some way of doing this by modelling a Hidden Markov Model that generates both sequences simultaneously and then deriving rules from that model, but I would not chose this approach unless I was already familiar with HMMs and had software to use as a starting point that I was happy to modify.
You can use text to speech to create sound waves. then compare sound waves with other's and match them with percentages.
This is my theory how Google has such a advanced spell checker.
Related
I have some text generated by some lousy OCR software.
The output contains mixture of words and space-separated characters, which should have been grouped into words. For example,
Expr e s s i o n Syntax
S u m m a r y o f T e r minology
should have been
Expression Syntax
Summary of Terminology
What algorithms can group characters into words?
If I program in Python, C#, Java, C or C++, what libraries provide the implementation of the algorithms?
Thanks.
Minimal approach:
In your input, remove the space before any single letter words. Mark the final words created as part of this somehow (prefix them with a symbol not in the input, for example).
Get a dictionary of English words, sorted longest to shortest.
For each marked word in your input, find the longest match and break that off as a word. Repeat on the characters left over in the original "word" until there's nothing left over. (In the case where there's no match just leave it alone.)
More sophisticated, overkill approach:
The problem of splitting words without spaces is a real-world problem in languages commonly written without spaces, such as Chinese and Japanese. I'm familiar with Japanese so I'll mainly speak with reference to that.
Typical approaches use a dictionary and a sequence model. The model is trained to learn transition properties between labels - part of speech tagging, combined with the dictionary, is used to figure out the relative likelihood of different potential places to split words. Then the most likely sequence of splits for a whole sentence is solved for using (for example) the Viterbi algorithm.
Creating a system like this is almost certainly overkill if you're just cleaning OCR data, but if you're interested it may be worth looking into.
A sample case where the more sophisticated approach will work and the simple one won't:
input: Playforthefunofit
simple output: Play forth efunofit (forth is longer than for)
sophistiated output: Play for the fun of it (forth efunofit is a low-frequency - that is, unnatural - transition, while for the is not)
You can work around the issue with the simple approach to some extent by adding common short-word sequences to your dictionary as units. For example, add forthe as a dictionary word, and split it in a post processing step.
Hope that helps - good luck!
I have a system that is confined to two alphanumeric characters. Some simple math shows that we get 1,296 combinations if we use all possible permutations 0-9 and a-z. Lower case letters cannot be distinguished from upper case, special characters (including a blank character) cannot be used.
Is there any creative mapping, perhaps to an external reference, to create a way to take this two character field significantly beyond 1,296 combinations?
Examples of identifers would be `00, OO, AZ, Z4, etc.'
Thanks!
I'm afraid not, no more than you could get a 3 bit number to represent more than 8 different numbers. If you're interested in the details you can look up information theory or Kolmogorov complexity. Essentially with only 1,296 combinations then you can only label 1,296 possible pieces of information.
As an example, consider if you had 1,297 things. All of those two letter combinations would take up the first 1,296 so what combination would be associated with the next one? It would have to be a repeat of something which you had earlier.
Shor also has some good material on this, and the implications of that sort of thing form the basis for a lot of file compression systems.
You could maybe squeeze out one more combination if you cheat, and allow a 'null' value to represent a different possibility, but thats not totally relevant to the idea of the question.
If you are restricted to two characters taken from an alphabet of 36, then you are limited to 36² distinct symbols, that's it.
More context is required to find workarounds, like stealing bits elsewhere, using symbols in pairs, breaking the case limitation, exploiting the history of transations...
The precise meaning of "a system that is confined to two alphanumeric characters" needs to be known to be able to suggest a workaround. Is that a space constraint? Do you need the restriction to 2 chars for efficiency? Does it need to work with other code that accepts or generates 2 char indexes?
If you have up to 1295 identifiers that are used often, and some others that occur only occasionally, you could choose an identifier, e.g. "ZZ", to indicate that another identifier is following. So "00" through to "ZY" would be 1295 simple 2-char identifiers, and "ZZ00" though to "ZZZZ" would be a further 1296 combined 4-char identifiers. (Or "ZZ0000" through to "ZZZZZZ" for a further 1296*1296 identifiers ...)
This could work for space constraints. For efficiency, it depends on whether the additional check to see if the identifier is "ZZ" is too expensive or not.
I'm writing a program that makes word declension for Polish language. In this language stems can vary in some cases (because of palatalization or mobile/fleeting e and other effects).
For example, we have word "karzeł" and it is basic dictionary form of word. It's stem is also 'karzeł'. But genitive form of this word is "karła" and stem is "karł". We can see here that 'e' dissapeared and 'rz' changes to 'r'.
Another example:
'uzda' -> stem 'uzd'
'uździe' -> stem 'uździ'
Alternation: 'zd' -> 'ździ'
I'd like to store in dictionary only basic form of stem ('karzeł' and 'uzd') and when I'll put in my program stem 'karł' or 'uździ' it will find proper basic stems. Alternations takes place only at the end of stem and contains maximum 4 letters of it.
Is there any algorithms that could do that? Levensthein distance treats all letters equally so if I type word 'barzeł' then the distance to stem 'karzeł' will be less than to stem 'karł'.
I thought also about neural networks but I'm not sure how to encode words (give each stem variation different id?).
Another idea is to write algorith which makes something like reversed alternation and creates set of possible stems and try to find them in dictionary.
I would like to highlight that I only want store basic form of stem and everything else makes on the fly.
First of all, I remember seeing a number of projects on Polish morphology around. So I would look at them first, before starting one of your own.
Regarding Levenshtein, as Pierre correctly noted in the comment, the distance function can be customized. And it should be. Let me put it this way: think of Levenshtein not as an algorithm of and in itself, but as a solution to a specific error model. First he suggests a model which says that when you are typing a word every letter can be either dropped or replaced by another one due to some random process (fingers not pressing the right keys). Then, his algorithm is just a generator of maximum likelihood solutions under this model. The more errors you allow, the smaller is the probability of this sequence of errors actually happening, the bigger is the score.
You (implicitly) state a very different hypothesis, though. That Polish stems may have certain flexibility at the end (some linguistic process that you do not fully understand within this framework). Then, when you strip your suffix (or something that looks like one), there are three options:
1) there is a chance that what you have here is just a different form of a stem you have stored in your dictionary, or
2) it is a completely different stem, or
3) you've stripped your suffix improperly and what you have is not stem at all.
You can heuristically estimate these probabilities by looking at how many letters in the beginning of the supposed stem match some dictionary entries, for example (how to find these entries is a related but different question). And then you can pick the guess that is the most plausible according to your metric/heuristic.
Now, note that you can use any algorithm to find the candidates in the dictionary. Including the Levenshtein algorithm - as long as you are reasonably sure that the right ones will be picked up. But obviously you are better off writing your own dictionary search algorithm that follows your own metric or emulates it. For example, by giving the biggest/prohibitive cost to the change of letters in the beginning of the word and reducing it as you go towards the end.
I'm looking at ways to deterministically replace unique strings with unique and optimally short replacements. So I have a finite set of strings, and the best compression I could achieve so far is through an enumeration algorithm, where I order the input set and then replace the strings with an enumeration of char strings over an extended alphabet (a..z, A...Z, aa...zz, aA... zZ, a0...z9, Aa..., aaa...zaa, aaA...zaaA, ....).
This works wonderfully as far as compression is concerned, but has the severe drawback that it is not atomic on any given input string. Rather, its result depends on knowing all input strings right from the start, and on the ordering of the input set.
Anybody knows of an algorithm that has similar compression but doesn't require knowing all input strings upfront?! Hashing for example would not work for me, as depending on the size of the input set I'd need a hash length of 8-12 for the hashes to be unique, and that would be too long as replacements (currently, the replacement strings are 1-3 chars long for my use cases (<10,000 input strings)). Also, if theoreticians among us know this is wasted effort, I would be interested to hear :-) .
You could use your enumeration scheme, but sorted by the order in which you first encounter the input strings.
For example, the first string you ever process can be mapped to "a".
The next distinct string would be mapped to "b", etc.
Every time you process a string, you'd need to look it up to see if it has already been mapped.
"Optimally short" depends on the population of strings from which your samples are drawn. In the absence of systematic redundancy in the population, you will find that only a fraction of arbitrary strings can be compressed at all (e.g., consider trying to compress random bit strings).
If you can make assumptions about your data, such as "the strings are expected to be mainly composed of English words" then you can do something simple and effective based on letter frequency (e.g., for English, the relative frequency order is something like ETAOINSHRDLUGCY..., so you would want to use fewer bits to represent Es and more bits to represent uncommon letters like Q).
Cheers.
I'm looking for an algorithm that can do a one-to-one mapping of a string onto another string.
I want an algorithm that given an alphabet I can perform a symmetric mapping function.
For example:
Let's consider that I have the alphabet "A","B","C","D","E","F". I want something like F("ABC") = "CEA" and F("CEA") = "ABC" for every N letter permutation.
Surely, an algorithm like this exists. If you know of an algorithm, please post the name of it and I can research it. If I haven't been clear enough in my request, please let me know.
Thanks in advance.
Edit 1:
I should clarify that I want enough entropy so that F("ABC") would equal "CEA" and F("CEA") = "ABC" but then I do NOT want F("ABD") to equal "CEF". Notice how two input letters stayed the same and the two corresponding output letters stayed the same?
So a Caesar Cipher/ROT13 or shuffling the array would not be sufficient. However, I don't need any "real" security. Just enough entropy for the output of the function to appear random. Weak encryption algorithms welcome.
Just create an array of objects that contain 2 fields -- a letter, and a random number. Sort the array. By the random numbers. This creates a mapping where the i-th letter of the alphabet now maps to the i-th letter in the array.
If simple transposition or substitution isn't quite enough, it sounds like you want to advance to a polyalphabetic cipher. The Vigenère cipher is extremely easy to implement in code, but is still difficult to break without using a computer.
I suggest the following.
Perform a dense coding of the input to positive integers - with an alphabet size of n and string length of m you can code the string into integers between zero and n^m - 1. In your example this would be the range [0,215]. Now perform a fixed involution on the encoded number and decode it again.
Take RC4, settle for some password, and you're done. (Not that this would be very safe.)
Take the set of all permutations of your alphabet, shuffle it, and map the first half of the set onto the second half. Bad for large alphabets, of course. :)
Nah, thought that over, I forgot about character repetitions. Maybe divide the input into chunks without repeating chars and apply my suggestion to all of those chunks.
I would restate your problem thus, and give you a strategy for that restatement:
"A substitution cypher where a change in input leads to a larger change in output".
The blocking of characters is irrelevant-- in the end, it's just mappings between numbers. I'll speak of letters here, but you can extend it to any block of n characters.
One of the easiest routes for this is a rotating substitution based on input. Since you already looked at the Vigenere cipher, it should be easy to understand. Instead of making the key be static, have it be dependent on the previous letter. That is, rotate through substitutions a different amount per each input.
The variable rotation satisfies the condition of making each small change push out to a larger change. Note that the algorithm will only push changes in one direction such that changes towards the end have smaller effects. You could run the algorithm both ways (front-to-back, then back-to-front) so that every letter of cleartext changed has the possibility of changing the entire string.
The internal rotation strategy elides the need for keys, while of course losing of most of the cryptographic security. It makes sense in context, though, as you are aiming for entropy rather than security.
You can solve this problem with Format-preserving encryption.
One Java-Library can be found under https://github.com/EVGStudents/FPE.git. There you can define a Regex and encrypt/decrypt string values matching this regex.