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I want to implement some applications with n-grams (preferably in PHP).
Which type of n-grams is more adequate for most purposes? A word level or a character level n-gram? How could you implement an n-gram-tokenizer in PHP?
First, I would like to know what N-grams exactly are. Is this correct? It's how I understand n-grams:
Sentence: "I live in NY."
word level bigrams (2 for n): "# I', "I live", "live in", "in NY", 'NY #'
character level bigrams (2 for n): "#I", "I#", "#l", "li", "iv", "ve", "e#", "#i", "in", "n#", "#N", "NY", "Y#"
When you have this array of n-gram-parts, you drop the duplicate ones and add a counter for each part giving the frequency:
word level bigrams: [1, 1, 1, 1, 1]
character level bigrams: [2, 1, 1, ...]
Is this correct?
Furthermore, I would like to learn more about what you can do with n-grams:
What do you think about my application approaches, especially the last one?
I hope you can help me. Thanks in advance!
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Applications that can be implemented efficiently and effectively using sets of n‐grams include spelling error detection and correction, query expansion, information retrieval with serial, inverted and signature files, dictionary look‐up, text compression, and language identification.
Language modeling is the way of determining the probability of any sequence of words. Language modeling is used in a wide variety of applications such as Speech Recognition, Spam filtering, etc.
N-grams are continuous sequences of words or symbols or tokens in a document. In technical terms, they can be defined as the neighbouring sequences of items in a document. They come into play when we deal with text data in NLP(Natural Language Processing) tasks.
An n-gram is a collection of n successive items in a text document that may include words, numbers, symbols, and punctuation. N-gram models are useful in many text analytics applications where sequences of words are relevant, such as in sentiment analysis, text classification, and text generation.
Word n-grams will generally be more useful for most text analysis applications you mention with the possible exception of language detection, where something like character trigrams might give better results. Effectively, you would create n-gram vector for a corpus of text in each language you are interested in detecting and then compare the frequencies of trigrams in each corpus to the trigrams in the document you are classifying. For example, the trigram the probably appears much more frequently in English than in German and would provide some level of statistical correlation. Once you have your documents in n-gram format, you have a choice of many algorithms for further analysis, Baysian Filters, N- Nearest Neighbor, Support Vector Machines, etc..
Of the applications you mention, machine translation is probably the most farfetched, as n-grams alone will not bring you very far down the path. Converting an input file to an n-gram representation is just a way to put the data into a format for further feature analysis, but as you lose a lot of contextual information, it may not be useful for translation.
One thing to watch out for, is that it isn't enough to create a vector [1,1,1,2,1] for one document and a vector [2,1,2,4] for another document, if the dimensions don't match. That is, the first entry in the vector can not be the in one document and is in another or the algorithms won't work. You will wind up with vectors like [0,0,0,0,1,1,0,0,2,0,0,1] as most documents will not contain most n-grams you are interested in. This 'lining up' of features is essential, and it requires you to decide 'in advance' what ngrams you will be including in your analysis. Often, this is implemented as a two pass algorithm, to first decide the statistical significance of various n-grams to decide what to keep. Google 'feature selection' for more information.
Word based n-grams plus Support Vector Machines in an excellent way to perform topic spotting, but you need a large corpus of text pre classified into 'on topic' and 'off topic' to train the classifier. You will find a large number of research papers explaining various approaches to this problem on a site like citeseerx. I would not recommend the euclidean distance approach to this problem, as it does not weight individual n-grams based on statistical significance, so two documents that both include the, a, is, and of would be considered a better match than two documents that both included Baysian. Removing stop-words from your n-grams of interest would improve this somewhat.
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