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courses:mapreduce-tutorial:step-14 [2012/01/25 15:46]
straka vytvořeno
courses:mapreduce-tutorial:step-14 [2012/01/31 16:08] (current)
dusek
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-====== MapReduce Tutorial :  ======+====== MapReduce Tutorial : Exercise - N-gram language model ====== 
 + 
 +For a given //N// create a simple N-gram language model. You can start experimenting on the following data: 
 +^ Path ^ Size ^ 
 +| /home/straka/wiki/cs-seq-medium | 8MB | 
 +| /home/straka/wiki/cs-seq | 82MB | 
 +| /home/straka/wiki/en-seq | 1.9GB | 
 + 
 +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. You can devise your own format, or you can use 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 a 0. Store the numbers using variable-length encoding (smaller numbers take less bytes) -- use ''pack 'w*', @word_numbers, 0''
 +  * One file of the resulting index should contain a sorted list of (N-gram representation, occurrences), where //N-gram representation// is described above and //occurrence// is a variable-length encoded number of occurrences (again using ''pack 'w', $occurrences''). No separators are necessary. 
 +  * Every data file should also be accompanied by an index file, which contains every 1000((You are free to choose better constant :--) ))-th //N-gram representation// of the data file, together with the byte offset of that //N-gram representation// in the data file. (The motivation behind the index file is that it will be read into memory and if an N-gram is searched for, it will point to the possible position in the data file.) 
 +  * As in the sorting example, the //N-gram representation// in one data file should be all smaller or larger than in another data file. 
 + 
 +Try creating such index. Ideally, the sizes of resulting data files should be as equal as possible. 
 + 
 +---- 
 + 
 +<html> 
 +<table style="width:100%"> 
 +<tr> 
 +<td style="text-align:left; width: 33%; "></html>[[step-13|Step 13]]: Sorting.<html></td> 
 +<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td> 
 +<td style="text-align:right; width: 33%; "></html>[[step-15|Step 15]]: K-means clustering.<html></td> 
 +</tr> 
 +</table> 
 +</html>

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