Several methods for determining a numerical distance between languages have been proposed in the literature. In this thesis I implement one of them, the ALINE distance, and develop a methodology for comparing its results with other language distance metrics. I then compare it with a leading distance metric, the LDND distance, proposed by the ASJP project.

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This thesis deals with a document classification, especially with a text classification method. Main goal of this thesis is to analyze two arbitrary document classification algorithms to describe them and to create an implementation of those algorithms. Chosen algorithms are Bayes classifier and classifier based on support vector machines (SVM) which were analyzed and implemented in the practical part of this thesis. One of the main goals of this thesis is to create and choose optimal text features, which are describing the input text best and thus lead to the best classification results. At the end of this thesis there is a bunch of tests showing comparison of efficiency of the chosen classifiers under various conditions.