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courses:mapreduce-tutorial:step-14 [2012/01/25 23:30] straka |
courses:mapreduce-tutorial:step-14 [2012/01/26 23:17] straka |
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Your model should contain all the unigrams, bigrams, ..., //N//-grams with the number of occurrences in the given corpus. | Your model should contain all the unigrams, bigrams, ..., //N//-grams with the number of occurrences in the given corpus. | ||
- | As the size of the resulting corpus matters, you should represent the //N//-grams efficiently. | + | As the size of the resulting corpus matters, you should represent the //N//-grams efficiently. |
* 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. | * 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 '' | + | * In order to represent //N//-gram, use the //N// numbers of the words, followed by a 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, | + | * 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// | + | * Every data file should also be accompanied by an index file, which contains every 1000-th //N-gram representation// |
- | * If the resulting index should | + | * As in the sorting example, the //N-gram representation// |
+ | |||
+ | Try creating such index. Ideally, the sizes of resulting data files should be as equal as possible. | ||