For a given N create a simple N-gram language model. You can experimenting on the following data:

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:

• 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 pack 'w*', @word_numbers, 0.
• One file of the resulting index should contain a sorted list of (N-gram representation, occurrence), where N-gram representation is described above and occurrence is a variable-length encoded number of occurrences (again using pack 'w', $occurences). No separators are necessary.
• Every data file should also be accompanied by the file index, which contains every 1000-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 file index is that it will be read into memory and if an N-gram is searched for, file index 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.