Abstract This paper innovatively charts the analogical influence of the modal auxiliaries on the regulation of periphrastic do in Early Modern English by means of Convolutional Neural Networks (CNNs), a flavour of connectionist models known for their applications in computer vision. CNNs can be harnessed to model the choice between competitors in a linguistic alternation by extracting not only the contexts a construction occurs in, but also the contexts it could have occurred in, but did not. Bearing on the idea that two forms are perceived as similar if they occur in similar contexts, the models provide us with pointers towards potential loci of analogical attraction that would be hard to retrieve otherwise. Our analysis reveals clear functional overlap between do and all modals, indicating not only that analogical pressure was highly likely, but even that affirmative declarative do functioned as a modal auxiliary itself throughout the late 16th century.

中文翻译：

---

一种联结主义的类比方法。论早期现代英语中外语do的情态意义

摘要 本文通过卷积神经网络 (CNN) 创新地描绘了模态辅助对调节早期现代英语中的周边行为的类比影响，这是一种以其在计算机视觉中的应用而闻名的连接主义模型。CNN 不仅可以提取结构出现的上下文，还可以提取它可能出现但没有出现的上下文，从而对语言交替中的竞争者之间的选择进行建模。考虑到如果两种形式出现在相似的上下文中就会被认为是相似的，这些模型为我们提供了指向潜在的类比吸引力位置的指针，否则这些位置很难检索。我们的分析揭示了 do 和所有模态之间明显的功能重叠，