Micro-cues of language evolution: A Multifactorial model of V2 loss in Central Romance

为什么语言会发生变化？像拉丁语这样功能齐全的语言是如何演变成法语或意大利语的？语序等核心特征的演变如何解释使语言变化成为可能的认知机制？通过本项目提出的新颖的重建方法和多因素模型，回答了这些基本问题。除了目前对语言变化的常规研究，即聚合来自不同地区的不同文本类型以返回不规则和拉长的变化曲线，我们的新校准方法(CAM)通过从给定地区选择特定的文本类型来控制变化，以识别稳定的进化证据。通过排除变化的非结构性因素，更规则的演化曲线被记录下来，因果模型可以被公式化和测试。通过对早期法语和意大利语中动词第二(V2)词序丧失的系统调查，对CAM进行了引导。从最早的可用文本到15世纪50年代，研究了诺曼底和威尼斯地区的法语和意大利语的一种特定的文本类型。在即将公开的扩展的双语注释语料库中进行的测试支持了一种新的语言变化的多因素模型(MUM)。其想法是，相关配置提供的多个线索使抽象的语法特征，如词序可学(LightFoot，2009)：由此得出的结论是，这些配置的变化会影响相关词序的流行和分布。我们根据文献和正在进行的工作选择的线索是断词、助词和宾语-动词语序。这些线索的行为变化被预测为预测V2词的丧失，这是通过每个现象的数量和质量演变来衡量的。对模型预测的验证是通过识别出现次数、注释其形态句法性质以及使用适用于整个时期的使用率的相关统计方法来实现的。MUM和CAM的结果提供了一、两种密切相关的语言中主要语法变化的比较行为和时间顺序。促进这些变化的线索的身份、轨迹和影响程度，III。对词序变化的一般因果模型的有效性的评估。在三个世纪的时间里，从两种语言的特定类型和地区收集带注释的文本语料库，并识别和加权其中的一些线索，这带来了高风险，带来了优化研究方法和显著加深对语言演变的理解的高收益。从两个站点的互补专业知识中构建协同效应，以开发更细粒度的概念框架，该项目使我们更接近理解语法更改是如何可能的。

Why do languages change? How does a fully functional language like Latin evolve to become e.g. French or Italian? And how does the evolution of central characteristics such as word order illuminate the cognitive mechanisms that make language change possible? These fundamental questions are answered through the novel reconstructive method and multifactorial model proposed by this project. Going beyond the current routine research into language change that aggregates different text types from diverse regions to return irregular and elongated curves of change, our new Calibrated Method (CaM) controls for variation by selecting specific text types from given regions to identify stable testimonies of evolution. By excluding non-structural factors of variation, more regular curves of evolution are documented, and causal models can be formulated and tested. CaM is piloted through the systematic investigation of the loss of verb second (V2) word order that characterizes early French and Italian. One specific text type is investigated from the Normandy and Venetian regions for French and Italian from the earliest available texts to the 1550s. The examination in the extended bilingual annotated corpus, to be made publicly available, supports a novel multifactorial model (MuM) of language change. The idea is that multiple cues provided by related configurations make abstract grammatical characteristics like word order learnable (Lightfoot 2009): it follows that changes in these configurations impact the currency and distribution of the relevant word order. The cues that we select on the basis of the literature and of ongoing work are asseveration particle si and Object-Verb word order. Changes in behavior of these cues are predicted to prefigure the loss of V2 word as measured by quantitative and qualitative evolution of each phenomenon. Verification of the predictions of the model is achieved by identification of occurrences, annotation of their morphosyntactic properties and use of correlational statistical methods applied to rates of use across the period. The results of the MuM and CaM provide i. the comparative behavior and chronology of a major grammatical change in two closely related languages, ii. the identity, trajectory and degree of influence of the cues that promote these changes, iii. an assessment of the validity of a general causal model of word order change. The high risks in assembling an annotated corpus of texts from specific types and regions for two languages over three centuries, and identifying and weighting some of the cues involved, bring about the high gains of optimizing research methods and significantly furthering the understanding of language evolution. Building synergy from the complementary expertise of both sites to develop a finer-grained conceptual framework, this project gets us one step closer to understanding how grammatical change is possible.