Question:
Given a piece of text like "This is a test"; how to build a machine learning model to get the number of word occurrences for example in this piece, word count is 4. After training, it is possible to predict text word count.
I know it is easy to write a program (like below pseudo code),
data: memory.punctuation['~', '`', '!', '#', '#', '$', '%', '^', '&', '*', ...]
f: count.word(text) -> count =
f: tokenize(text) --list-->
f: count.token(list, filter) where filter(token)<not in memory.punctuation> -> count
however in this question, we require to use machine learning algorithm. I wonder how machine can learn the concept of count (currently, we know machine learning is good at classification). Any idea and suggestions? Thanks in advance.
Failures:
We can use sth like word2vec (encoder) to build word vectors; if we consider seq2seq approach, we can train sth like This is a test <s> 4 <e> This is very very long sentence and the word count is greater than ten <s> 4 1 <e> (4 1 to represent the number 14). However, it does not work since attention model is used to get similar vector for example text translating (This is a test --> 这(this) 是(is) 一个(a) 测试(test)). It is hard to find relationship between [this ...] and 4 which is an aggregated number (i.e. model not convergent).
We know machine learning is good at classification. If we treat "4" as a class, the number of classes is infinite; if we do a tricky and use count/text.length as prediction, i have not got a model that fit even training data set (model not convergent); for example, if we use many short sentence to train the model, it will fail to predict long sentence length. And it may be related to an information paradox: we can encode data in a book as 0.x and use a machine to to mark a position on a rod to split it into 2 parts with length a and b, where a/b = 0.x; but we cannot find a machine.
What about a regression problem?
I think it would work quite well and that at the end it will output a nearly whole numbers all the time.
Also you can train a simple RNN to do the job, assuming you are using a hot one encoding and take an output from the last state.
If V_h is all zeros but the space index (which will be 1) and V_x as well, than the network will actually sum the spaces, and if c is 1 at the end so the output will be the number of words - For every length!
I think we can take it as a classification problem for a character being the input and if word breaker as the output.
In other words, at some time point t, we output whether the input character at the same time point is a word breaker (YES) or not (NO). If yes, then increase the word count. If no, then read the next character.
In modern English language I don't think there are going to be long words. So simple RNN model should do perhaps without the concern of vanishing gradient.
Let me know what you think!
Use NLTK for counting words,
from nltk.tokenize import word_tokenize
text = "God is Great!"
word_count = len(word_tokenize(text))
print(word_count)
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!
Tesseract OCR engine sometimes outputs text that has no meaning, i want to design an algorithm that neglects any text or word that has no meaning, below is some sort of output text that i want to neglect,my simple solution is to count the words in the recognized text that's separated by " " and the text which has too many words will be garbage(Hint: i'm scanning images which at most will contains 40 words) any idea will be helpful,thanks.
wo:>"|axnoA1wvw\
ldflfig
°J!9O‘ !P99W M9N 6 13!-|15!Cl ‘I-/Vl
978 89l9 Z0 3+ 3 'l9.l.
97 999 VLL lLOZ+ 3 9l!q°lN
wo0'|axno/(#|au1e>1e: new;
1=96r2a1ey\1 1uauud0|e/\e(]
|8UJB){ p8UJL|\7'
Divide the output text into words. Divide the words into triples. Count the triple frequencies, and compare to triple frequencies from text of a known-good text corpus (EG all the articles from some mailing list discussing what you intend to OCR, minus the header lines).
When I say "triples", I mean:
whe, hen, i, say, tri, rip, ipl, ple, les, i, mea, ean
...so "i" has a frequency of 2 in this short example, while the others are all frequency 1.
If you do a frequency count of each of these triples for a large document in your intended language, it should become possible to be reasonably accurate in guessing whether a string is in the same language.
Granted, it's heuristic.
I've used a similar approach for detecting English passwords in a password changing program. It worked pretty well, though there's no such thing as a perfect "obvious password rejecter".
Check the words against a dictionary?
Of course, this will have false-positives for things like foreign-phrases or code. The problem in general is intractable (ex. is this code or gibberish? :) ). The only (nearly) perfect method would be to use this as a heuristic to flag certain sections for human review.
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.
I receive encoded PDF files regularly. The encoding works like this:
the PDFs can be displayed correctly in Acrobat Reader
select all and copy the test via Acrobat Reader
and paste in a text editor
will show that the content are encoded
so, examples are:
13579 -> 3579;
hello -> jgnnq
it's basically an offset (maybe swap) of ASCII characters.
The question is how can I find the offset automatically when I have access to only a few samples. I cannot be sure whether the encoding offset is changed. All I know is some text will usually (if not always) show up, e.g. "Name:", "Summary:", "Total:", inside the PDF.
Thank you!
edit: thanks for the feedback. I'd try to break the question into smaller questions:
Part 1: How to detect identical part(s) inside string?
You need to brute-force it.
If those patterns are simple like +2 character code like in your examples (which is +2 char codes)
h i j
e f g
l m n
l m n
o p q
1 2 3
3 4 5
5 6 7
7 8 9
9 : ;
You could easily implement like this to check against knowns words
>>> text='jgnnq'
>>> knowns=['hello', '13579']
>>>
>>> for i in range(-5,+5): #check -5 to +5 char code range
... rot=''.join(chr(ord(j)+i) for j in text)
... for x in knowns:
... if x in rot:
... print rot
...
hello
Is the PDF going to contain symbolic (like math or proofs) or natural language text (English, French, etc)?
If the latter, you can use a frequency chart for letters (digraphs, trigraphs and a small dictionary of words if you want to go the distance). I think there are probably a few of these online. Here's a start. And more specifically letter frequencies.
Then, if you're sure it's a Caesar shift, you can grab the first 1000 characters or so and shift them forward by increasing amounts up to (I would guess) 127 or so. Take the resulting texts and calculate how close the frequencies match the average ones you found above. Here is information on that.
The linked letter frequencies page on Wikipedia shows only letters, so you may want to exclude them in your calculation, or better find a chart with them in it. You may also want to transform the entire resulting text into lowercase or uppercase (your preference) to treat letters the same regardless of case.
Edit - saw comment about character swapping
In this case, it's a substitution cipher, which can still be broken automatically, though this time you will probably want to have a digraph chart handy to do extra analysis. This is useful because there will quite possibly be a substitution that is "closer" to average language in terms of letter analysis than the correct one, but comparing digraph frequencies will let you rule it out.
Also, I suggested shifting the characters, then seeing how close the frequencies matched the average language frequencies. You can actually just calculate the frequencies in your ciphertext first, then try to line them up with the good values. I'm not sure which is better.
Hmmm, thats a tough one.
The only thing I can suggest is using a dictionary (along with some substitution cipher algorithms) may help in decoding some of the text.
But I cannot see a solution that will decode everything for you with the scenario you describe.
Why don't you paste some sample input and we can have ago at decoding it.
It's only possible then you have a lot of examples (examples count stops then: possible to get all the combinations or just an linear values dependency or idea of the scenario).
also this question : How would I reverse engineer a cryptographic algorithm? have some advices.
Do the encoded files open correctly in PDF readers other than Acrobat Reader? If so, you could just use a PDF library (e.g. PDF Clown) and use it to programmatically extract the text you need.
I am working on a project that requires the parsing of log files. I am looking for a fast algorithm that would take groups messages like this:
The temperature at P1 is 35F.
The temperature at P1 is 40F.
The temperature at P3 is 35F.
Logger stopped.
Logger started.
The temperature at P1 is 40F.
and puts out something in the form of a printf():
"The temperature at P%d is %dF.", Int1, Int2"
{(1,35), (1, 40), (3, 35), (1,40)}
The algorithm needs to be generic enough to recognize almost any data load in message groups.
I tried searching for this kind of technology, but I don't even know the correct terms to search for.
I think you might be overlooking and missed fscanf() and sscanf(). Which are the opposite of fprintf() and sprintf().
Overview:
A naïve!! algorithm keeps track of the frequency of words in a per-column manner, where one can assume that each line can be separated into columns with a delimiter.
Example input:
The dog jumped over the moon
The cat jumped over the moon
The moon jumped over the moon
The car jumped over the moon
Frequencies:
Column 1: {The: 4}
Column 2: {car: 1, cat: 1, dog: 1, moon: 1}
Column 3: {jumped: 4}
Column 4: {over: 4}
Column 5: {the: 4}
Column 6: {moon: 4}
We could partition these frequency lists further by grouping based on the total number of fields, but in this simple and convenient example, we are only working with a fixed number of fields (6).
The next step is to iterate through lines which generated these frequency lists, so let's take the first example.
The: meets some hand-wavy criteria and the algorithm decides it must be static.
dog: doesn't appear to be static based on the rest of the frequency list, and thus it must be dynamic as opposed to static text. We loop through a few pre-defined regular expressions and come up with /[a-z]+/i.
over: same deal as #1; it's static, so leave as is.
the: same deal as #1; it's static, so leave as is.
moon: same deal as #1; it's static, so leave as is.
Thus, just from going over the first line we can put together the following regular expression:
/The ([a-z]+?) jumps over the moon/
Considerations:
Obviously one can choose to scan part or the whole document for the first pass, as long as one is confident the frequency lists will be a sufficient sampling of the entire data.
False positives may creep into the results, and it will be up to the filtering algorithm (hand-waving) to provide the best threshold between static and dynamic fields, or some human post-processing.
The overall idea is probably a good one, but the actual implementation will definitely weigh in on the speed and efficiency of this algorithm.
Thanks for all the great suggestions.
Chris, is right. I am looking for a generic solution for normalizing any kind of text. The solution of the problem boils down to dynmamically finding patterns in two or more similar strings.
Almost like predicting the next element in a set, based on the previous two:
1: Everest is 30000 feet high
2: K2 is 28000 feet high
=> What is the pattern?
=> Answer:
[name] is [number] feet high
Now the text file can have millions of lines and thousands of patterns. I would like to parse the files very, very fast, find the patterns and collect the data sets that are associated with each pattern.
I thought about creating some high level semantic hashes to represent the patterns in the message strings.
I would use a tokenizer and give each of the tokens types a specific "weight".
Then I would group the hashes and rate their similarity. Once the grouping is done I would collect the data sets.
I was hoping, that I didn't have to reinvent the wheel and could reuse something that is already out there.
Klaus
It depends on what you are trying to do, if your goal is to quickly generate sprintf() input, this works. If you are trying to parse data, maybe regular expressions would do too..
You're not going to find a tool that can simply take arbitrary input, guess what data you want from it, and produce the output you want. That sounds like strong AI to me.
Producing something like this, even just to recognize numbers, gets really hairy. For example is "123.456" one number or two? How about this "123,456"? Is "35F" a decimal number and an 'F' or is it the hex value 0x35F? You're going to have to build something that will parse in the way you need. You can do this with regular expressions, or you can do it with sscanf, or you can do it some other way, but you're going to have to write something custom.
However, with basic regular expressions, you can do this yourself. It won't be magic, but it's not that much work. Something like this will parse the lines you're interested in and consolidate them (Perl):
my #vals = ();
while (defined(my $line = <>))
{
if ($line =~ /The temperature at P(\d*) is (\d*)F./)
{
push(#vals, "($1,$2)");
}
}
print "The temperature at P%d is %dF. {";
for (my $i = 0; $i < #vals; $i++)
{
print $vals[$i];
if ($i < #vals - 1)
{
print ",";
}
}
print "}\n";
The output from this isL
The temperature at P%d is %dF. {(1,35),(1,40),(3,35),(1,40)}
You could do something similar for each type of line you need to parse. You could even read these regular expressions from a file, instead of custom coding each one.
I don't know of any specific tool to do that. What I did when I had a similar problem to solve was trying to guess regular expressions to match lines.
I then processed the files and displayed only the unmatched lines. If a line is unmatched, it means that the pattern is wrong and should be tweaked or another pattern should be added.
After around an hour of work, I succeeded in finding the ~20 patterns to match 10000+ lines.
In your case, you can first "guess" that one pattern is "The temperature at P[1-3] is [0-9]{2}F.". If you reprocess the file removing any matched line, it leaves "only":
Logger stopped.
Logger started.
Which you can then match with "Logger (.+).".
You can then refine the patterns and find new ones to match your whole log.
#John: I think that the question relates to an algorithm that actually recognises patterns in log files and automatically "guesses" appropriate format strings and data for it. The *scanf family can't do that on its own, it can only be of help once the patterns have been recognised in the first place.
#Derek Park: Well, even a strong AI couldn't be sure it had the right answer.
Perhaps some compression-like mechanism could be used:
Find large, frequent substrings
Find large, frequent substring patterns. (i.e. [pattern:1] [junk] [pattern:2])
Another item to consider might be to group lines by edit-distance. Grouping similar lines should split the problem into one-pattern-per-group chunks.
Actually, if you manage to write this, let the whole world know, I think a lot of us would like this tool!
#Anders
Well, even a strong AI couldn't be sure it had the right answer.
I was thinking that sufficiently strong AI could usually figure out the right answer from the context. e.g. Strong AI could recognize that "35F" in this context is a temperature and not a hex number. There are definitely cases where even strong AI would be unable to answer. Those are the same cases where a human would be unable to answer, though (assuming very strong AI).
Of course, it doesn't really matter, since we don't have strong AI. :)
http://www.logparser.com forwards to an IIS forum which seems fairly active. This is the official site for Gabriele Giuseppini's "Log Parser Toolkit". While I have never actually used this tool, I did pick up a cheap copy of the book from Amazon Marketplace - today a copy is as low as $16. Nothing beats a dead-tree-interface for just flipping through pages.
Glancing at this forum, I had not previously heard about the "New GUI tool for MS Log Parser, Log Parser Lizard" at http://www.lizardl.com/.
The key issue of course is the complexity of your GRAMMAR. To use any kind of log-parser as the term is commonly used, you need to know exactly what you're scanning for, you can write a BNF for it. Many years ago I took a course based on Aho-and-Ullman's "Dragon Book", and the thoroughly understood LALR technology can give you optimal speed, provided of course that you have that CFG.
On the other hand it does seem you're possibly reaching for something AI-like, which is a different order of complexity entirely.