Fuzzy string match - groovy
After running optical char recognition on some images, I get approximate text. Often the recognition is not great. For instance, the actual text "DATE" comes as "DHTE" or "0HTE". Basically I need to identify and extract the data in each line, so i don't want perfect recognition, just enough to identify the date line. I tried to calculate the Levenshtein edit distance, but unfortunately this tends to give similar values for DATE and TIME. At the moment, I'm trying to explore if I can match the data patterns using regular expressions instead.
Is there a method/algorithm to better the matching process? Fortunately, my set of words is not very large.
(i'm using tesseract for ocr and groovy/java for the algorithm)
This one has a few pretty cool algorithms
http://secondstring.sourceforge.net/
This is a basic one in StringUtils
levenstein distance
Related
Use the polarity distribution of word to detect the sentiment of new words
I have just started a project in NLP. Suppose I have a graph for each word that shows the polarity distribution of sentiments for that word in different sentences. I want to know what I can use to recognize the feelings of new words? Any other use you have in mind I will be happy to share. I apologize for any possible errors in my writing. Thanks a lot
Assuming you've got some words that have been hand-labeled with positive/negative sentiments, but then you encounter some new words that aren't labeled: If you encounter the new words totally alone, outside of contexts, there's not much you can do. (Maybe, you could go out to try to find extra texts with those new words, such as vis dictionaries or the web, then use those larger texts in the next approach.) If you encounter the new words inside texts that also include some of your hand-labeled words, you could try guessing that the new words are most like the words you already know that are closest-to, or used-in-the-same-places. This would leverage what's called "the distributional hypothesis" – words with similar distributions have similar meanings – that underlies a lot of computer natural-language analysis, including word2vec. One simple thing to try along these lines: across all your texts, for every unknown word U, tally up the counts all neighboring words within N positions. (N could be 1, or larger.) From that, pick the top 5 words occuring most often near the unknown word, and look up your prior labels, and avergae them together (perhaps weighted by the number of occurrences.) You'll then have a number for the new word. Alternatively, you could train a word2vec set-of-word-vectors for all of your texts, including the unknown & know words. Then, ask that model for the N most-similar neighbors to your unknown word. (Again, N could be small or large.) Then, from among those neighbors with known labels, average them together (again perhaps weighted by similarity), to get a number for the previously unknown word. I wouldn't particularly expect either of these techniques to work very well. The idea that individual words can have specific sentiment is somewhat weak given the way that in actual language, their meaning is heavily modified, or even reversed, by the surrounding grammar/context. But in each case these simple calculate-from-neighbors techniqyes are probably better than random guesses. If your real aim is to calculate the overall sentiment of longer texts, like sentences, paragraphs, reviews, etc, then you should discard your labels of individual words an acquire/create labels for full texts, and apply real text-classification techniques to those larger texts. A simple word-by-word approach won't do very well compared to other techniques – as long as those techniques have plenty of labeled training data.
NLP: Curating definitional summaries for a specific term from textbook
I would like to be able to curate definitional summaries for a specific term from a textbook. For example, from a Biology textbook, I would like to be able form a concise summary for the word "mitochondria". I have tried this by first parsing through the textbook for all sentences that contain the word "mitochondria", and feeding those sentences through summarization algorithms such as TextRank and LexRank, but those algorithms were not able to determine "definitional" sentences that well. By definitional summaries, I mean useful sentences as far as a definition goes. For example, the sentence "The mitochondria is the powerhouse of the cell" would be a definitional sentence while the sentence "Fungal cells also contain mitochondria and a complex system of internal membranes, including the endoplasmic reticulum and Golgi apparatus" is not really pertinent to the definition of the mitochondria. Any help or leads would be very much appreciated
There isn't really a straightforward way to do this, but you do have some options: Just use a regex for "mitochondria is". It is the stupidest possible thing, but given a textbook it might prove satisfactory. It's simple enough testing should be easy, and at worst provides a baseline to compare alternatives to. Run a parser (example: Stanford Parser) on each sentence with the word "mitochondria", and extract sentences where mitochondria is the subject. This would eliminate the negative example you gave. You would have to tune this, perhaps restricting main verbs, accounting for coordinators, and so on. Use Information Extraction (example: Stanford OpenIE) to get a list of facts about mitochondria (like is-in(mitochondria, cell)) and do something with that.
This is a very open ended question. I can try to point how I would approach this... One way would be to use some kind of vector representation for text (word2vec or sent2vec come to mind). Then by encoding the average of the sentences in vector format and checking the cosine similarity of this and of the term you seek, you could be getting something close to the definitional sentences you seek. Even testing the cosine similarity of the averaged sentences you get out of the summary algorithm and the term might get you close to judge how close you are
String-matching algorithm for noisy text
I have used OCR (optical character recognition) to get texts from images. The images contain book covers. Because of the images are so noisy, some characters are misrecognised, or some noises are recognised as a character. Examples: "w COMPUTER Nnwonxs i I "(Compuer Networks) "s.ll NEURAL NETWORKS C "(Neural Networks) "1llllll INFRODUCIION ro PROBABILITY ti iitiiili My "(Introduction of Probability) I builded a dictionary with words, but i want to somehow match the recognised text with the dictionary. I tried LCS (Longest Common subsequence), but its not so effective. What is the best string matching algorithm for this kind of problem? (So a part of string is just noise, but also the important part of string can has some misrecognised characters)
That's really a big question. Followings are something I know about it. For more details, you can read some related papers. For single word, use Hamming Distance to calculate the similarity between the word your recognized by OCR and those in your dictionary; this step is used to correct the the words have been recognized by OCR but do not exist. Eg： If the result of OCR is INFRODUCIION which dosen't exist in your dictionary, you can find out the Hamming Distance of word 'INTRODUCTION' is 2. So it may be mis-recognized as 'INFRODUCIION'. However, the same word may be recognized as different words with the same Hamming Distance between them. Eg： If the result of OCR is the CAY, you may find CAR and CAT are both with the same Hamming Distance of 1, so that will be confused. In this case, there are several things can be used for analyze: Still for single word, the image different between CAT and CAY is less that CAR and CAY. So for this reason, CAT seems the right word with a greater probability. Then let us the context to caculate another probability. If the whold sentence is 'I drove my new CAY this morning', as for people usually drive a CAR but not a CAT, we have a better chance to regard the word CAY as CAR but not CAT. For the frequency of the words used in the similar articles, use TF-TDF.
Are you saying you have a dictionary that defines all words that are acceptable? If so, it should be fairly straight forward to take each word and find the closest match in your dictionary. Set a match threshold and discard the word if it does not reach the threshold. I would experiment with the Soundex and Metaphone algorithms or the Levenshtein Distance algorithm.
I need a function that describes a set of sequences of zeros and ones?
I have multiple sets with a variable number of sequences. Each sequence is made of 64 numbers that are either 0 or 1 like so: Set A sequence 1: 0,0,0,0,0,0,1,1,0,0,0,0,1,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0 sequence 2: 0,0,0,0,1,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 sequence 3: 0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0 ... Set B sequence1: 0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1 sequence2: 0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0 ... I would like to find a mathematical function that describes all possible sequences in the set, maybe even predict more and that does not contain the sequences in the other sets. I need this because I am trying to recognize different gestures in a mobile app based on the cells in a grid that have been touched (1 touch/ 0 no touch). The sets represent each gesture and the sequences a limited sample of variations in each gesture. Ideally the function describing the sequences in a set would allow me to test user touches against it to determine which set/gesture is part of. I searched for a solution, either using Excel or Mathematica, but being very ignorant about both and mathematics in general I am looking for the direction of an expert. Suggestions for basic documentation on the subject is also welcome.
It looks as if you are trying to treat what is essentially 2D data in 1D. For example, let s1 represent the first sequence in set A in your question. Then the command ArrayPlot[Partition[s1, 8]] produces this picture: The other sequences in the same set produce similar plots. One of the sequences from the second set produces, in response to the same operations, the picture: I don't know what sort of mathematical function you would like to define to describe these pictures, but I'm not sure that you need to if your objective is to recognise user gestures. You could do something much simpler, such as calculate the 'average' picture for each of your gestures. One way to do this would be to calculate the average value for each of the 64 pixels in each of the pictures. Perhaps there are 6 sequences in your set A describing gesture A. Sum the sequences element-by-element. You will now have a sequence with values ranging from 0 to 6. Divide each element by 6. Now each element represents a sort of probability that a new gesture, one you are trying to recognise, will touch that pixel. Repeat this for all the sets of sequences representing your set of gestures. To recognise a user gesture, simply compute the difference between the sequence representing the gesture and each of the sequences representing the 'average' gestures. The smallest (absolute) difference will direct you to the gesture the user made. I don't expect that this will be entirely foolproof, it may well result in some user gestures being ambiguous or not recognisable, and you may want to try something more sophisticated. But I think this approach is simple and probably adequate to get you started.
In Mathematica the following expression will enumerate all the possible combinations of {0,1} of length 64.
Tuples[{1, 0}, {64}]
But there are 2^62 or 18446744073709551616 of them, so I'm not sure what use that will be to you.
Maybe you just wanted the unique sequences contained in each set, in that case all you need is the Mathematica Union[] function applied to the set. If you have a the sets grouped together in a list in Mathematica, say mySets, then you can apply the Union operator to every set in the list my using the map operator.
Union/#mySets
If you want to do some type of prediction a little more information might be useful.
Thanks you for the clarifications.
Machine Learning
The task you want to solve falls under the disciplines known by a variety of names, but probably most commonly as Machine Learning or Pattern Recognition and if you know which examples represent the same gestures, your case would be known as supervised learning.
Question: In your case do you know which gesture each example represents ?
You have a series of examples for which you know a label ( the form of gesture it is ) from which you want to train a model and use that model to label an unseen example to one of a finite set of classes. In your case, one of a number of gestures. This is typically known as classification.
Learning Resources
There is a very extensive background of research on this topic, but a popular introduction to the subject is machine learning by Christopher Bishop.
Stanford have a series of machine learning video lectures Standford ML available on the web.
Accuracy
You might want to consider how you will determine the accuracy of your system at predicting the type of gesture for an unseen example. Typically you train the model using some of your examples and then test its performance using examples the model has not seen. The two of the most common methods used to do this are 10 fold Cross Validation or repeated 50/50 holdout. Having a measure of accuracy enables you to compare one method against another to see which is superior.
Have you thought about what level of accuracy you require in your task, is 70% accuracy enough, 85%, 99% or better?
Machine learning methods are typically quite sensitive to the specific type of data you have and the amount of examples you have to train the system with, the more examples, generally the better the performance.
You could try the method suggested above and compare it against a variety of well proven methods, amongst which would be Random Forests, support vector machines and Neural Networks. All of which and many more are available to download in a variety of free toolboxes.
Toolboxes
Mathematica is a wonderful system, is infinitely flexible and my favourite environment, but out of the box it doesn't have a great deal of support for machine learning.
I suspect you will make a great deal of progress more quickly by using a custom toolbox designed for machine learning. Two of the most popular free toolboxes are WEKA and R both support more than 50 different methods for solving your task along with methods for measuring the accuracy of the solutions.
With just a little data reformatting, you can convert your gestures to a simple file format called ARFF, load them into WEKA or R and experiment with dozens of different algorithms to see how each performs on your data. The explorer tool in WEKA is definitely the easiest to use, requiring little more than a few mouse clicks and typing some parameters to get started.
Once you have an idea of how well the established methods perform on your data you have a good starting point to compare a customised approach against should they fail to meet your criteria.
Handwritten Digit Recognition
Your problem is similar to a very well researched machine learning problem known as hand written digit recognition. The methods that work well on this public data set of handwritten digits are likely to work well on your gestures.
Finding related texts(correlation between two texts)
I'm trying to find similar articles in database via correlation. So i split text in array of words, then delete frequently used words (articles,pronouns and so on), then compare two text with pearson coefficient function. For some text it's works but for other it's not so good(texts with large text have higher coefficient). Can somebody advice a good method to find related texts?
Some of the problems you mention boild down to normalizing over document length and overall word frequency. Try tf-idf.
First and foremost, you need to specify what you precisely mean by similarity and when two documents are (more/less) similar. If the similarity you are looking for is literal, then I would vectorise the documents using term frequencies, and use the cosine similarity to liken them to each other given that texts are inherently directional data. tf-idf and log-entropy weighting schemes may be tested depending on your use-case. The edit distance is inefficient with long texts. If you care more about the semantics, word embeddings are your ally.