We study whether five scaling properties (given by Zipf's law, Heaps' law, Ebeling's method, Taylor's law, and longrange correlation analysis) can serve for evaluation of computational models.
we test n-gram language models, a probabilistic context-free grammar, language models based on Simon/Pitman-Yor processes, neural language models, and generative adversarial networks for text generation.