some verbal forms. 137 A drawback of the proposed method is that it cannot be applied effectively 138 to very short texts. The algorithm needs to find reoccurring word sequences 139 in order to extract dictionaries of a relevant size, which are needed in order 140 to find patterns shared with other dictionaries. Therefore, the compression 141 of the initial part of a string is not effective: we estimated empirically 1000 142 tokens or words to be a reasonable size for learning the model of a document 143 and to be effective in its compression. 144 The FCD as described in the previous section can be effectively employed 146 in tasks like authorship and plagiarism analysis. We report in this section 147 experiments on four datasets written in English, Italian, and German. 148 We consider a dataset of English texts known as Federalist Papers, a col- 150 lection of 85 political articles written by Alexander Hamilton, James Madi- 151 son and John Jay, published in 1787-88 under the anonymous pseudonym 152 ‘Publius’. This corpus is particularly interesting, as Hamilton and Madison 153 claimed later the authorship of their texts, but a number of essays (the ones 154 numbered 49-58 and 62-63) have been claimed by both of them. This is a 155 classical dataset employed in the early days of authorship attribution liter- 156 ature, as the candidate authors are well-defined and the texts are uniform 157 8 3. Experimental Results 145 3.1. The Federalist Papers 149