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    <p>It is called an N-gram; in your case a 4-gram. It can indeed be obtained as the by-product of a Markov-chain, but you could also use a sliding window (size 4) to walk through the (linear) text while updating a 4-dimensionsal "histogram". </p> <p>UPDATE 2011-11-22: A markov chain is a way to model the probability of switching to a new state, given the current state. This is the stochastic equivalent of a "state machine". In the natural language case, the "state" is formed by the "previous N words", which implies that you consider the prior probability (before the previous N words) as equal_to_one. Computer people will most likely use a tree for implementing Markov chains in the NLP case. The "state" is simply the path taken from the root to the current node, and the probabilities of the words_to_follow are the probabilities of the current node's offspring. But every time that we choose a new child node, we actually shift down the tree, and "forget" the root node, out window is only N words wide, which translates to N levels deep into the tree.</p> <p>You can easily see that if you are walking a Markov chain/tree like this, at any time the probability before the first word is 1, the probability after the first word is P(w1), after the second word = P(w2) || w1, etc. So, when processing the corpus you build a Markov tree ( := update the frequencies in the nodes), at the end of the ride you can estimate the probability of a given choice of word by freq(word) / SUM(freq(siblings)). For a word 5-deep into the tree this is the <strong>probability of the word, given the previous 4 words</strong>. If you want the N-gram probabilities, you want the <strong>product of all the probabilities in the path</strong> from the root to the last word.</p>
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    1. POWhich algorithm I can use to find common adjacent words/ pattern recognition?
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    1. POWhich algorithm I can use to find common adjacent words/ pattern recognition?
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    1. COBTW: my toy-project http://sourceforge.net/projects/wakkerbot/ used Markov chains (inherited from Megahal http://megahal.alioth.debian.org/ )
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    2. COFour words was only an example, I want to find another like 2 or 3, or more, 5, 6, 7, until a defined number.
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      1. USRenato Dinhani
    3. CO2 or 3 can be obtained by aggregating, once you have the 4 component array. You can, of course use a larger window+array, but be prepared to get _very_ large histograms. Storing (and retrieving) a 10 dimensional array will be a major effort. Probably a tree would be optimal at some point (wakkerbot uses a 5-level / 5-gram tree with N-ary hashtables on tokennumber in every node) Most nodes are narrow, only the rootnode is full-width. Storing the tree in a recursive database-table seems possible and is scalable, but would probably perform bad, typically 100 ms / query.
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