do you know any good algorithms that match two strings and then return a percentage in how many percent those two strings match?
And are there some, that work with databases too?
The Levenstein distance is such a measure. It basically tells you how many characters need to be edited, deleted or added, to get from the first to the second string. I'm not sure whether some database systems support that.
But I know for sure that a much more simplified algorithm named Soundex is supported in some database systems.
It depends upon your criteria for similarity. Other people have already referred you to Levenstein distance (edit distance is the same thing). That's usually pretty good, and definitely more language-independent than something like soundex. However, be aware that Levenstein difference does not handle transposition very well. Thus:
Levenstein("copy", "cpoy") == 2
If you're trying to deal with human input, transpositions are fairly common. Whether that's a problem or not depends on your metrics for similarity.
It's been a while, but I believe Postgresql has levenstein() either built-in or available as a contrib C module.
I think the problem you're looking for is called Edit Distance. It is expensive to compute in general, but if you are looking for strings within small edit distance of other strings, it is not so bad. There is more information in the Wikipedia article.
How to best match two strings? Have them go out for coffee, and if they hit it off, dinner and a movie. Or maybe they could do some peer programming? It depends on the strings, really. Even coffee can often be tricky.
Would this be of help? I just ran into it. Comparing Two Strings producing a numeric delta
Related
I work at a public health department that takes in and stores lots of medical data every day. I've written a program that uses regular expressions to determine if particular fields in the incoming data are valid or invalid. Ex: DOBs come in as YYYYmmDD, so they should match regex ^[0-9]{8}$
I want to analyze the "invalid" data to help identify problems in our system (we get way too much data to go through each 'bad' record row-by-row). Can anyone suggest AI techniques/machine learning techniques that can 'monitor' the bad data and find patterns in what is wrong? I think that coming up with a bunch of regular expressions for possible ways the data could be invalid (ex. not enough or too many characters) and then keeping track of those results might work. But instead of me thinking up all of the ways the data could be invalid, I'm curious about ways to 'learn' the patterns from the bad data using AI.
Are there any known techniques that do this?
I think that coming up with a bunch of regular expressions for possible ways the data could be invalid (ex. not enough or too many characters) and then keeping track of those results might work. But instead of me thinking up all of the ways the data could be invalid, I'm curious about ways to 'learn' the patterns from the bad data using AI.
What's funny is I'm reminded of a quotation usually attributed to Jamie Zawinski:
Some people, when confronted with a problem, think "I know, I'll use
regular expressions." Now they have two problems.
Except, in this case, I think the hand-crafted regex route is actually your best bet!
Irony of ironies.
Anyway.
The point of this saying is that people tend to overcomplicate their solutions. Here, regexs are actually a fairly simple solution to your problem, whereas creating a learner is something that will take you a lot more time than I think you realize.
There are fewer ways for this very constrained data representation (a date) to be expressed correctly, than there are ways for it to be expressed incorrectly. Because there are infinite ways to define bad data. You want to train a learner to detect all of them? It's a rabbit hole. Think of this AI learner instead as a coworker or a friend: how would you describe to them all the ways that dates can't be represented properly?
While your intention was to make less work for yourself in the long run -- and that's a good quality to have -- figuring out how to develop a learner, not to mention train and validate it, not to mention watch it carefully, outweigh any benefits that learner can provide you in such a narrow use case.
Bayesian filtering might be what you are looking for.
It sounds like you want to apply supervised learning to regular expressions. These fellows seem to be up to something of that sort.
Perhaps look for techniques of "outlier detection"?
I'm looking through large arrays for particular sequences and I feel like I'm approaching the problem using brute force rather than computer science.
Currently I'm looking sequentially down the large array for the first item in the search sequence, then checking each item after that until a failure or a complete match. This provides 100% accuracy but it's not very fast with large arrays.
I was never a computer science student so I missed out on many algorithm classes that plenty of people around here probably had. Is there a better way to search for sequences in arrays? I'm not necessarily interested in perfect accuracy if it makes a difference.
How about using the Boyer-Moore algorithm? It's fairly simple and straightforward, and can increase the practical speed quite a lot, especially if your target sequence is fairly long. It's meant for searching for strings, but that's just a particular type of array of course.
There is no better way to search the array itself for candidates for matches. If you have no order you cannot discard candidates as a match or not without considering them.
That being said you can optimize candidate acceptance or rejection utilizing the method suggested by Janne.
If you need to search for many patterns in the same sequence you can use suffix arrays
If you have to search for one pattern then you can improve a little over brute force with Boyer-Moore or Knuth-Morris-Pratt
I would like to have a word (e.g. "Apple) and process a text (or maybe more). I'd like to come up with related terms. For example: process a document for Apple and find that iPod, iPhone, Mac are terms related to "Apple".
Any idea on how to solve this?
As a starting point: your question relates to text mining.
There are two ways: a statistical approach, and one form natural language processing (nlp).
I do not know much about nlp, but can say something about the statistical approach:
You need some vector space representation of your documents, see
http://en.wikipedia.org/wiki/Vector_space_model
http://en.wikipedia.org/wiki/Document-term_matrix
http://en.wikipedia.org/wiki/Tf%E2%80%93idf
In order to learn semantics, that is: different words mean the same, or one word can have different meanings, you need a large text corpus for learning. As I said this is a statistical approach, so you need lots of samples.
http://www.daviddlewis.com/resources/testcollections/
Maybe you have lots of documents from the context you are going to use. That is the best situation.
You have to retrieve latent factors from this corpus. Most common are:
LSA (http://en.wikipedia.org/wiki/Latent_semantic_analysis)
PLSA (http://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis)
nonnegative matrix factorization (http://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
latent dirichlet allocation (http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)
These methods involve lots of math. Either you dig it, or you have to find good libraries.
I can recommend the following books:
http://www.oreilly.de/catalog/9780596529321/toc.html
http://www.oreilly.de/catalog/9780596516499/index.html
Like all of AI, it's a very difficult problem. You should look into natural language processing to learn about some of the issues.
One very, very simplistic approach can be to build a 2d-table of words, with for each pair of words the average distance (in words) that they appear in the text. Obviously you'll need to limit the maximum distance considered, and possibly the number of words as well. Then, after processing a lot of text you'll have an indicator of how often certain words appear in the same context.
What I would do is get all the words in a text and make a frequency list (how often each word appears). Maybe also add to it a heuristic factor on how far the word is from "Apple". Then read multiple documents, and cross out words that are not common in all the documents. Then prioritize based on the frequency and distance from the keyword. Of course, you will get a lot of garbage and possibly miss some relevant words, but by adjusting the heuristics you should get at least some decent matches.
The technique that you are looking for is called Latent Semantic Analysis (LSA). It is also sometimes called Latent Semantic Indexing. The technique operates on the idea that related concepts occur together in text. It uses statistics to build the word relationships. Given a large enough corpus of documents it will definitely solve your problem of finding related words.
Take a look at vector space models.
Questions
I want to classify/categorize/cluster/group together a set of several thousand websites. There's data that we can train on, so we can do supervised learning, but it's not data that we've gathered and we're not adamant about using it -- so we're also considering unsupervised learning.
What features can I use in a machine learning algorithm to deal with multilingual data? Note that some of these languages might not have been dealt with in the Natural Language Processing field.
If I were to use an unsupervised learning algorithm, should I just partition the data by language and deal with each language differently? Different languages might have different relevant categories (or not, depending on your psycholinguistic theoretical tendencies), which might affect the decision to partition.
I was thinking of using decision trees, or maybe Support Vector Machines (SVMs) to allow for more features (from my understanding of them). This post suggests random forests instead of SVMs. Any thoughts?
Pragmatical approaches are welcome! (Theoretical ones, too, but those might be saved for later fun.)
Some context
We are trying to classify a corpus of many thousands of websites in 3 to 5 languages (maybe up to 10, but we're not sure).
We have training data in the form of hundreds of websites already classified. However, we may choose to use that data set or not -- if other categories make more sense, we're open to not using the training data that we have, since it is not something we gathered in the first place. We are on the final stages of scraping data/text from websites.
Now we must decide on the issues above. I have done some work with the Brown Corpus and the Brill tagger, but this will not work because of the multiple-languages issue.
We intend to use the Orange machine learning package.
According to the context you have provided, this is a supervised learning problem.
Therefore, you are doing classification, not clustering. If I misunderstood, please update your question to say so.
I would start with the simplest features, namely tokenize the unicode text of the pages, and use a dictionary to translate every new token to a number, and simply consider the existence of a token as a feature.
Next, I would use the simplest algorithm I can - I tend to go with Naive Bayes, but if you have an easy way to run SVM this is also nice.
Compare your results with some baseline - say assigning the most frequent class to all the pages.
Is the simplest approach good enough? If not, start iterating over algorithms and features.
If you go the supervised route, then the fact that the web pages are in multiple languages shouldn't make a difference. If you go with, say lexical features (bag-o'-words style) then each language will end up yielding disjoint sets of features, but that's okay. All of the standard algorithms will likely give comparable results, so just pick one and go with it. I agree with Yuval that Naive Bayes is a good place to start, and only if that doesn't meet your needs that try something like SVMs or random forests.
If you go the unsupervised route, though, the fact that the texts aren't all in the same language might be a big problem. Any reasonable clustering algorithm will first group the texts by language, and then within each language cluster by something like topic (if you're using content words as features). Whether that's a bug or a feature will depend entirely on why you want to classify these texts. If the point is to group documents by topic, irrespective of language, then it's no good. But if you're okay with having different categories for each language, then yeah, you've just got as many separate classification problems as you have languages.
If you do want a unified set of classes, then you'll need some way to link similar documents across languages. Are there any documents in more that one language? If so, you could use them as a kind of statistical Rosetta Stone, to link words in different languages. Then, using something like Latent Semantic Analysis, you could extend that to second-order relations: words in different languages that don't ever occur in the same document, but which tend to co-occur with words which do. Or maybe you could use something like anchor text or properties of the URLs to assign a rough classification to documents in a language-independent manner and use that as a way to get started.
But, honestly, it seems strange to go into a classification problem without a clear idea of what the classes are (or at least what would count as a good classification). Coming up with the classes is the hard part, and it's the part that'll determine whether the project is a success or failure. The actual algorithmic part is fairly rote.
Main answer is: try different approaches. Without actual testing it's very hard to predict what method will give best results. So, I'll just suggest some methods that I would try first and describe their pros and cons.
First of all, I would recommend supervised learning. Even if the data classification is not very accurate, it may still give better results than unsupervised clustering. One of the reasons for it is a number of random factors that are used during clustering. For example, k-means algorithm relies on randomly selected points when starting the process, which can lead to a very different results for different program runnings (though x-means modifications seems to normalize this behavior). Clustering will give good results only if underlying elements produce well separated areas in the feature space.
One of approaches to treating multilingual data is to use multilingual resources as support points. For example, you can index some Wikipedia's articles and create "bridges" between same topics in different languages. Alternatively, you can create multilingual association dictionary like this paper describes.
As for methods, the first thing that comes to mind is instance-based semantic methods like LSI. It uses vector space model to calculate distance between words and/or documents. In contrast to other methods it can efficiently treat synonymy and polysemy. Disadvantage of this method is a computational inefficiency and leak of implementations. One of the phases of LSI makes use of a very big cooccurrence matrix, which for large corpus of documents will require distributed computing and other special treatment. There's modification of LSA called Random Indexing which do not construct full coocurrence matrix, but you'll hardly find appropriate implementation for it. Some time ago I created library in Clojure for this method, but it is pre-alpha now, so I can't recommend using it. Nevertheless, if you decide to give it a try, you can find project 'Clinch' of a user 'faithlessfriend' on github (I'll not post direct link to avoid unnecessary advertisement).
Beyond special semantic methods the rule "simplicity first" must be used. From this point, Naive Bayes is a right point to start from. The only note here is that multinomial version of Naive Bayes is preferable: my experience tells that count of words really does matter.
SVM is a technique for classifying linearly separable data, and text data is almost always not linearly separable (at least several common words appear in any pair of documents). It doesn't mean, that SVM cannot be used for text classification - you still should try it, but results may be much lower than for other machine learning tasks.
I haven't enough experience with decision trees, but using it for efficient text classification seems strange to me. I have seen some examples where they gave excellent results, but when I tried to use C4.5 algorithm for this task, the results were terrible. I believe you should get some software where decision trees are implemented and test them by yourself. It is always better to know then to suggest.
There's much more to say on every topic, so feel free to ask more questions on specific topic.
I need to implement some kind of metric space search in Postgres(*) (PL or PL/Python). So, I'm looking for good sources (or papers) with a very clear and crisp explanation of the machinery behind these ideas, in such way that I can implement it myself.
I would prefer clarity over efficiency.
(*) The need for that is described better here.
Especially for geographical data, look at PostGIS first to see if you need to implement anything. If you do, start with the papers listed in the Wikipedia entry on GiST.
Looking at your link, it seems your metric space is strings with some sort of edit distance as the metric. A nice but oldish overview of some solutions is given by Navarro, Baeza-Yates, Sutinen, and Tarhio, IEEE Data Engineering Bulletin, 2001; the related papers on Citeseer could also be useful. Locality Sensitive Hashing is a newer technique that might be useful, but a lot of the papers are heavy on math.
BK-Trees are useful for indexing and searching anything that obeys the triangle inequality, metric spaces included. The canonical example is searching for strings within a given edit distance of a target. I wrote an article about that here.
Unfortunately, there's no built in support for this in Postgres. You could implement it yourself using GIST, but obviously that'll be a lot of work. I can't think of any way to implement it without writing your own indexes short of storing the tree in a table, which obviously isn't going to be very efficient.
You can try http://sisap.org where many modern metric indexes are listed, including BK-trees. You can find code in C to try different alternatives.
Some techniques that involve space search that might help you are Hill-Climbing, Neural Network Training, Genetic Algorithm, and Particle Swarm.
You will also need to define a distance metric over your metric space. Have you done so?(& out of curiosity, what is it, if you have done so)