Lietuviškų tekstų klasifikavimas į verstinius ir originalius pagal jų žodžių dažnių skirstinius

Translated text has certain features which mark it as such, which can be identified using statistical methods. Features such as lexical density, vocabulary richness and word length distribution are some of the marks of translated text identified by existing research. In this work support vector machine models, which were found to be effective for this purpose by previous studies, are applied to corpora of Lithuanian monolingual texts. The models are then augmented using variables constructed to reflect the suggested marks of translated text in an attempt to improve classification performance