Title: Approximation ability of Transformer networks for functions with various smoothness of Besov spaces: error analysis and token extraction. Abstract: Although Transformer networks outperform various natural language processing tasks, many aspects of their theoretical nature are still unclear. On the other hand, fully connected neural networks have been extensively studied in terms of their approximation and estimation capability where the target function is included in such function classes as H\"older class and Besov class. In this paper, we study the approximation and estimation error of Transformer networks in a setting where the target function takes a fixed-length sentence as an input and belongs to two variants of Besov spaces known as anisotropic Besov and mixed smooth Besov spaces, in which it is shown that Transformer networks can avoid curse of dimensionality. By overcoming the difficulties in limited interactions among tokens, we prove that Transformer networks can accomplish minimax optimal rate. Our result also shows that token-wise parameter sharing in Transformer networks decreases dependence of the network width on the input length. Moreover, we prove that, under suitable situations, Transformer networks dynamically select tokens to pay careful attention to. This phenomenon matches attention mechanism, on which Transformer networks are based. Our analyses strongly support the reason why Transformer networks have outperformed various natural language processing tasks from a theoretical perspective.