标题： 后续词语的转移概率中语法的作用 显示英文标题

标题： The role of grammar in transition-probabilities of subsequent words in English text

摘要： 句子形成是一个高度结构化、依赖历史且样本空间减少（SSR）的过程。 虽然句子的第一个词可以从整个词汇中选择，但通常随着句子的进展，后续词语的选择自由度会越来越多地受到语法和上下文的限制。 这种样本空间减少的特性为词频中的齐普夫定律提供了一个自然的解释，但它无法捕捉英语文本中词与词之间的转移概率矩阵的结构。 在这里，我们采用一种观点，即语法约束（如主语-谓语-宾语）在以SSR方式生成词语的句子中局部地重新排列词语顺序。 我们证明，在一个样本空间减少过程中叠加语法结构——作为一种局部词语重新排列（排列）过程——足以解释词频和词与词之间转移概率。 我们将语法有序的SSR模型在再现真实文本的多个测试统计量方面的质量与其他文本生成模型进行了比较，例如伯努利模型、西蒙模型和猴子打字模型。 显示英文摘要

摘要： Sentence formation is a highly structured, history-dependent, and sample-space reducing (SSR) process. While the first word in a sentence can be chosen from the entire vocabulary, typically, the freedom of choosing subsequent words gets more and more constrained by grammar and context, as the sentence progresses. This sample-space reducing property offers a natural explanation of Zipf's law in word frequencies, however, it fails to capture the structure of the word-to-word transition probability matrices of English text. Here we adopt the view that grammatical constraints (such as subject--predicate--object) locally re-order the word order in sentences that are sampled with a SSR word generation process. We demonstrate that superimposing grammatical structure -- as a local word re-ordering (permutation) process -- on a sample-space reducing process is sufficient to explain both, word frequencies and word-to-word transition probabilities. We compare the quality of the grammatically ordered SSR model in reproducing several test statistics of real texts with other text generation models, such as the Bernoulli model, the Simon model, and the Monkey typewriting model.