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courses:mapreduce-tutorial:step-14 [2012/01/25 22:19] straka |
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| ====== MapReduce Tutorial : Exercise - N-gram language model ====== | ====== MapReduce Tutorial : Exercise - N-gram language model ====== | ||
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| + | For a given //N// create a simple N-gram language model. You can experimenting on the following data: | ||
| + | ^ Path ^ Size ^ | ||
| + | | / | ||
| + | | / | ||
| + | | / | ||
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| + | Your model should contain all the unigrams, bigrams, ..., //N//-grams with the number of occurrences in the given corpus. | ||
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| + | As the size of the resulting corpus matters, you should represent the //N//-grams efficiently. Try using the following representation: | ||
| + | * Compute the unique words of the corpus, filter out the words that have only one occurrence, sort them according to the number of their occurrences and number them from 1. | ||
| + | * In order to represent //N//-gram, use the //N// numbers of the words, followed by 0. Store the numbers using variable-length encoding (smaller numbers take less bytes) -- use '' | ||
| + | * One file of the resulting index should contain a sorted list of (n-gram representation, | ||
| + | * Every file should also be accompanied by the index -- the index contains every 1000-th //n-gram representation// | ||
| + | * If the resulting index should consist of | ||
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