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Both sides previous revision Previous revision 2012/01/31 16:08 dusek 2012/01/28 23:14 majlis Added links to previous and next chapter.2012/01/26 23:18 straka 2012/01/26 23:18 straka 2012/01/26 23:17 straka 2012/01/26 23:16 straka 2012/01/26 23:15 straka 2012/01/26 23:14 straka 2012/01/25 23:37 straka 2012/01/25 23:36 straka 2012/01/25 23:35 straka 2012/01/25 23:30 straka 2012/01/25 23:15 straka 2012/01/25 22:19 straka 2012/01/25 15:46 straka vytvořeno Go		2012/01/31 16:08 dusek 2012/01/28 23:14 majlis Added links to previous and next chapter.2012/01/26 23:18 straka 2012/01/26 23:18 straka 2012/01/26 23:17 straka 2012/01/26 23:16 straka 2012/01/26 23:15 straka 2012/01/26 23:14 straka 2012/01/25 23:37 straka 2012/01/25 23:36 straka 2012/01/25 23:35 straka 2012/01/25 23:30 straka 2012/01/25 23:15 straka 2012/01/25 22:19 straka 2012/01/25 15:46 straka vytvořeno Go Next revision Both sides next revision
courses:mapreduce-tutorial:step-14 [2012/01/25 22:19] straka		courses:mapreduce-tutorial:step-14 [2012/01/25 23:15] straka
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	====== MapReduce Tutorial : Exercise - N-gram language model ======		====== MapReduce Tutorial : Exercise - N-gram language model ======
		+
		+	For a given //N// create a simple N-gram language model. You can 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. Try using the following representation:
		+	* Find the unique words of the corpus, sort them according to the number of their occurences
		+

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