What is the difference between Tokenization and Segmentation in NLP. I searched about them but I didn't really find any differences
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Short answer: All tokenization is segmentation, but not all segmentation is tokenization.
Long Answer:
While segmentation is a more generic concept of splitting the input text, tokenization is a type of segmentation and it is carried out based on a well defined criteria.
For example - in a hypothetical scenario if all your input sentences are compound sentences of two sub-sentences, then splitting them into two independent sentences can be termed as segmentation (but not tokenization). Tokenization is a form of segmentation which is performed on the basis of a semantic criteria or using a token dictionary - e.g. a word or sub-word tokenization, mainly with an intention of assigning them token ids for downstream processing.
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I have recently had the need to create an ANTLR language grammar for the purpose of a transpiler (Converting one scripting language to another). It occurs to me that Google Translate does a pretty good job translating natural language. We have all manner of recurrent neural network models, LSTM, and GPT-2 is generating grammatically correct text.
Question: Is there a model sufficient to train on grammar/code example combinations for the purpose of then outputting a new grammar file given an arbitrary example source-code?
I doubt any such model exists.
The main issue is that languages are generated from the grammars and it is next to impossible to convert back due to the infinite number of parser trees (combinations) available for various source-codes.
So in your case, say you train on python code (1000 sample codes), the resultant grammar for training will be the same. So, the model will always generate the same grammar irrespective of the example source code.
If you use training samples from a number of languages, the model still can't generate the grammar as it consists of an infinite number of possibilities.
Your example of Google translate works for real life translation as small errors are acceptable, but these models don't rely on generating the root grammar for each language. There are some tools that can translate programming languages example, but they don't generate the grammar, work based on the grammar.
Update
How to learn grammar from code.
After comparing to some NLP concepts, I have a list of issue that may arise and a way to counter them.
Dealing with variable names, coding structures and tokens.
For understanding the grammar, we'll have to breakdown the code to its bare minimum form. This means understanding what each and every term in the code means. Have a look at this example
The already simple expression is reduced to the parse tree. We can see that the tree breaks down the expression and tags each number as a factor. This is really important to get rid of the human element of the code (such as variable names etc.) and dive into the actual grammar. In NLP this concept is known as Part of Speech tagging. You'll have to develop your own method to do the tagging, by it's easy given that you know grammar for the language.
Understanding the relations
For this, you can tokenize the reduced code and train using a model based on the output you are looking for. In case you want to write code, make use of a n grams model using LSTM like this example. The model will learn the grammar, but extracting it is not a simple task. You'll have to run separate code to try and extract all the possible relations learned by the model.
Example
Code snippet
# Sample code
int a = 1 + 2;
cout<<a;
Tags
# Sample tags and tokens
int a = 1 + 2 ;
[int] [variable] [operator] [factor] [expr] [factor] [end]
Leaving the operator, expr and keywords shouldn't matter if there is enough data present, but they will become a part of the grammar.
This is a sample to help understand my idea. You can improve on this by having a deeper look at the Theory of Computation and understanding the working of the automata and the different grammars.
What you're describing is 'just' learning structure of Context-Free Grammars.
I'm not sure if this approach will actually work for your case, but it's a long-standing problem in NLP: grammar induction for Context-Free Grammars. An example introduction how to tackle this problem using statistical learning methods can be found in Charniak's Statistical Language Learning.
Note that what I described is about CFGs in general, but you might want to check induction for LL grammars, because parser generators mostly use these types of grammars.
I know nothing about ANTLR, but there are pretty good examples of translating natural language e.g. into valid SQL requests: http://nlpprogress.com/english/semantic_parsing.html#sql-parsing.
I am working on a way to represent C/C++ program code. in order to create a dataset and do some machine learning after that.
Thinking about code as text and doing some text mining doesn't seem correct for me. because i'm more interesseted by the semantic and precision of calculations.
So what could be a good representative vector of programms ?
Thanks.
I take it that you don't want to represent your programs as sequences of tokens.
Remember you don't have to actually represent code as words. If you're interested in semantic relationships you can use higher-level descriptions - for example you can use parse trees of expressions rather than tokens.
You can also take this grammatical approach further and represent the whole program as parse tree in some grammar rather than a sequence of tokens. There are recurrent networks that can handle tree-structured data.
sequence to sequence learning is a powerful mechanism for language translation, especially using it locally in a context specific case.
I am following this pytorch tutorial for the task.
However, the tutorial did not split the data into training and testing.
You might think its not a big deal, just split it up, use one chunk for training and the other for testing. But it is not that simple.
Essentially, the tutorial creates the indices of the seen words while leading the dataset. The indices are simply stored in the dictionary. This is before going to the encoder RNN, just a simple conversion kind of task from words to the numbers.
If data is split up at random, what happens is, one of the keyword may not appear in the sentences from the training set, and so may not have an index at all. If it shows up at the time of testing, what should be done?
Extend the dictionary?
Sequence to sequence models performance strongly depend on count of unique words in vocabulary. Each unique word has to be encountered a number of times in training set, such that model can learn it correct usage. Words that appears few times cannot be used by the model, as model can't learn enough information about such words. In practice, the size of the dictionary is usually reduced, replacing the rare words with a special "UNK" token. Therefore, if a new word occurs during testing, it can be assumed that it is rare (since it never appears in the training set) and replace it with "UNK".
I'm working on a project that is trying to use context-free grammars for parsing images. We are trying to construct trees of image segments, then use machine learning to parse images using these visual grammars.
I have found SVM-CFG which looks ideal, the trouble is that it is designed for string parsing, where each terminal in the string has at most two neighbors (the words before and after). In our visual grammar, each segment can be next to an arbitrary number of other segments.
What is the best way to parse these visual grammars? Specifically, can I encode my data to use SVM-CFG? Or am I going to have to write my own Kernel/parsing library?
SVM-CFG is a specific implementation of the cutting plane optimization algorithm used in SVM-struct (described here http://www.cs.cornell.edu/People/tj/publications/tsochantaridis_etal_04a.pdf, Section 4).
At each step, the cutting plane algorithm calls a function to find the highest scoring structured output assignment (in SVM-CFG this is the highest scoring parse).
For one-dimensional strings, SVM-CFG runs a dynamic programming algorithm to find the highest scoring parse in polynomial time.
You could extend SVM-struct to return the highest scoring parse for an image, but no polynomial-time algorithm exists to do this!
Here is a reference for a state-of-the-art technique that parses images: http://www.socher.org/uploads/Main/SocherLinNgManning_ICML2011.pdf. They run into the same problem for finding the highest scoring parse of an image segmentation, so they use a greedy algorithm to find an approximate solution (see section 4.2). You might be able to incorporate a similar greedy algorithm into SVM-struct.
Binarization is the act of transforming colorful features of of an entity into vectors of numbers, most often binary vectors, to make good examples for classifier algorithms.
If we where to binarize the sentence "The cat ate the dog", we could start by assigning every word an ID (for example cat-1, ate-2, the-3, dog-4) and then simply replace the word by it's ID giving the vector <3,1,2,3,4>.
Given these IDs we could also create a binary vector by giving each word four possible slots, and setting the slot corresponding to a specific word with to one, giving the vector <0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,1>. The latter method is, as far as I know, is commonly referred to as the bag-of-words-method.
Now for my question, what is the best binarization method when it comes to describe features for natural language processing in general, and transition-based dependency parsing (with Nivres algorithm) in particular?
In this context, we do not want to encode the whole sentence, but rather the current state of the parse, for example the top word on the stack en the first word in the input queue. Since order is highly relevant, this rules out the bag-of-words-method.
With best, I am referring to the method that makes the data the most intelligible for the classifier, without using up unnecessary memory. For example I don't want a word bigram to use 400 million features for 20000 unique words, if only 2% the bigrams actually exist.
Since the answer is also depending on the particular classifier, I am mostly interested in maximum entropy models (liblinear), support vector machines (libsvm) and perceptrons, but answers that apply to other models are also welcome.
This is actually a really complex question. The first decision you have to make is whether to lemmatize your input tokens (your words). If you do this, you dramatically decrease your type count, and your syntax parsing gets a lot less complicated. However, it takes a lot of work to lemmatize a token. Now, in a computer language, this task gets greatly reduced, as most languages separate keywords or variable names with a well defined set of symbols, like whitespace or a period or whatnot.
The second crucial decision is what you're going to do with the data post-facto. The "bag-of-words" method, in the binary form you've presented, ignores word order, which is completely fine if you're doing summarization of a text or maybe a Google-style search where you don't care where the words appear, as long as they appear. If, on the other hand, you're building something like a compiler or parser, order is very much important. You can use the token-vector approach (as in your second paragraph), or you can extend the bag-of-words approach such that each non-zero entry in the bag-of-words vector contains the linear index position of the token in the phrase.
Finally, if you're going to be building parse trees, there are obvious reasons why you'd want to go with the token-vector approach, as it's a big hassle to maintain sub-phrase ids for every word in the bag-of-words vector, but very easy to make "sub-vectors" in a token-vector. In fact, Eric Brill used a token-id sequence for his part-of-speech tagger, which is really neat.
Do you mind if I ask what specific task you're working on?
Binarization is the act of
transforming colorful features of
an entity into vectors of numbers,
most often binary vectors, to make
good examples for classifier
algorithms.
I have mostly come across numeric features that take values between 0 and 1 (not binary as you describe), representing the relevance of the particular feature in the vector (between 0% and 100%, where 1 represents 100%). A common example for this are tf-idf vectors: in the vector representing a document (or sentence), you have a value for each term in the entire vocabulary that indicates the relevance of that term for the represented document.
As Mike already said in his reply, this is a complex problem in a wide field. In addition to his pointers, you might find it useful to look into some information retrieval techniques like the vector space model, vector space classification and latent semantic indexing as starting points. Also, the field of word sense disambiguation deals a lot with feature representation issues in NLP.
[Not a direct answer] It all depends on what you are try to parse and then process, but for general short human phrase processing (e.g. IVT) another method is to use neural networks to learn the patterns. This can be very acurate for smallish vocubularies