When processing a sequence of length $n$, a one-dimensional Convolutional Neural Network (CNN) treats the text as a 'one-dimensional image', extracting local features such as $n$-grams. For a convolutional layer with a kernel size of $k$ and $d$ input and output channels, the computational complexity is $\mathcal{O}(knd^2)$. Because CNNs process local regions simultaneously and use a hierarchical structure, they require only $\mathcal{O}(1)$ sequential operations, enabling parallel computation. Furthermore, the hierarchical architecture allows the receptive field to expand; for instance, a two-layer CNN with $k = 3$ connects distant tokens like $\mathbf{x}_1$ and $\mathbf{x}_5$, resulting in a maximum path length of $\mathcal{O}(n/k)$.