Using Cross Language Analysis to Investigate Factors for Differential Marking

使用跨语言分析来研究差异标记的因素

基本信息

批准号：
1953296
负责人：
Shobhana Chelliah
金额：
$ 34.35万
依托单位：
University of North Texas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-15 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953296&HistoricalAwards=false
关键词：
Using Cross Language Analysis Investigate

项目摘要

This project uses cross language analysis to investigate factors for 'differential marking.' In linguistics, 'differential marking' refers to morphological patterning where a nominal, such as a subject, occurs with special encoding. With this encoding, a speaker can communicate non-grammatical information about that nominal, such as surprise, unpredictability, or unexpectedness of the involvement of an entity in an event. Speakers do not consciously use differential marking to package information. Rather, there appear to be a complex combination of grammatical, discourse, semantic, and pragmatic factors that predict differential marking, including inherent properties of the noun (e.g., person, animacy, or count versus mass), properties of the predicate (transitivity, completed action), or the position of a nominal in longer connected speech (e.g., mentioned for the first time in a conversation or story). The project will include the training of students in coding and grammatical analysis. Language data, the coding protocol, and Python-based tools will be archived and freely accessible at University of North Texas Digital Library and/or through a GitHub repository.This project uses an innovative documentary method to gather information on factors determining differential marking. First, native speaking linguists of the investigated languages will code nominals in connected discourse for factors associated with differential marking. When non-speakers analyze data for differential marking, nuanced meanings can be lost in translation. Coding by trained native speakers will more accurately capture the meanings intended by the speaker. The project will develop a coding manual to standardize coding. Second, discussions about the data with groups of non-linguist speakers will be used to refine coding. Group discussions on grammar tend to evoke scenarios of usage and interpretation that are not recalled by investigators working on data on their own. Third, the project will develop Python-based tools to compare factors for differential marking across various data sets, both within one language and across different languages, to find statistically salient correspondences between factors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目使用跨语言分析来研究“差异标记”的因素。在语言学中，“差异标记”是指在特殊编码中出现标称（例如主题）的形态图案。有了这个编码，演讲者可以传达有关该名义的非语法信息，例如惊喜，不可预测性或实体参与事件的意外性。扬声器不会有意识地使用差异标记来包装信息。相反，语法，话语，语义和务实因素似乎具有复杂的结合，这些因素可以预测差异标记，包括名词的固有属性（例如，人，动画或计数与群众），谓词的属性（过渡性，完成的动作），或在更连续的语音中的名称的位置（例如，在更连接的语音中提到了第一次对话），或者是第一个对话（例如，都在对话中，或者是第一次对话。该项目将包括对学生进行编码和语法分析的培训。语言数据，编码协议和基于Python的工具将在北德克萨斯大学数字图书馆和/或通过GITHUB存储库来归档，并可以自由访问。本项目使用创新的文献方法来收集有关确定差异标记的因素的信息。首先，调查语言的本地说话语言学家将在连接的话语中为标称与差异标记相关的因素编写名义。当非扬声器分析数据以进行差异标记时，翻译中可能会失去细微的含义。由训练有素的母语人士编码将更准确地捕获说话者意图的含义。该项目将开发一份编码手册来标准化编码。其次，将使用非语言说话者组的数据讨论来完善编码。关于语法的小组讨论倾向于唤起使用和解释的情况，而研究人员独自研究数据并未回忆起。第三，该项目将开发基于Python的工具，以比较在一种语言和不同语言中的各种数据集之间进行差异标记的因素，以找到因素之间的统计上显着的对应关系。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识绩效和更广泛影响的评估来通过评估来进行支持。