CAREER: Sociolinguistic Structure Induction

语言的主要功能之一是管理社会关系。然而，我们目前的许多语言技术都未能解释语言在不同社会背景下的变化，或者它如何取决于说话者的态度和身份。因此，现有的语言技术是脆弱的变化，特别是在非标准的方言和非正式的社会背景。这个CAREER项目旨在通过构建数据驱动的计算算法和表示来缩小这一差距，这些算法和表示能够在大型文本和语音语料库中识别社会意义。研究范围包括当地社会变量，例如用于调节谈话中的正式程度的称呼语（“女士”、“老兄”），以及大型社区中群体层面的方言差异。研究的预期成果包括：（1）研究语言社会功能的新的计算方法和相应的新见解;（2）利用这些见解实现新的计算语言学应用，并提高现有技术的鲁棒性;（3）计算机科学和社会科学之间的更好的接触，以增强研究人员所需的工具，以便访问大型-该项目利用无监督结构归纳技术来识别语言现象和社会现象之间的潜在结构。社会语言学结构可以从各种各样的数据源中挖掘出来，包括社交媒体和数字化人文档案。该项目旨在从多个细节层面研究语言和社会现象之间的相互作用：（1）宏观层面的社会语言学结构，如驱动方言变异的交叉社会身份;（2）微观层面的社会语言学结构，如调节对话正式程度的一系列称呼语;（3）动态社会语言学结构，如新词、拼写和句法结构在社交媒体上的传播。识别这些社会语言学结构需要新的学习算法和表征，由社会科学的理论见解驱动。该项目的研究结果在以计算和社会科学界为目标的场所传播，外联和教育部分包括在这些社区之间建立新的桥梁。

One of the principal functions of language is to manage social relationships. Yet much of our current language technology fails to account for how language varies across social contexts, or how it depends on speakers' attitudes and identities. As a consequence, existing language technology is brittle to variation, particularly in non-standard dialects and informal social contexts. This CAREER project is aimed at closing this gap, by building data-driven computational algorithms and representations that are capable of identifying social meaning in large corpora of text and speech. The scope of the research includes local social variables, such as the address terms used to modulate formality in conversation ("Ms", "dude"), as well as group-level dialect differences across large communities. The expected results of the research include: (1) new computational methods for studying language's social function and, correspondingly, new insights; (2) leveraging these insights to enable new computational linguistic applications, and to improve the robustness of existing technology; (3) better engagement between computer science and the social sciences in augmenting the tools that researchers require in order to access large-scale naturally occurring social and linguistic data; and (4) improved education on the analysis and interpretation of such data for undergraduate and graduate students and the public at large.This project uses unsupervised structure induction techniques to identify the latent structures that connect linguistic and social phenomena. Sociolinguistic structures can be mined from a diverse array of data sources, including social media and digitized humanities archives. The project aims at interactions between linguistic and social phenomena on multiple levels of detail: (1) macro-level sociolinguistic structures, such as the intersecting social identities that drive dialect variation; (2) micro-level sociolinguistic structures, such as the array of address terms that modulate formality in dialogue; and (3) dynamic sociolinguistic structures, such as the transmission of new words, orthographies, and syntactic constructions across social media. Identifying these sociolinguistic structures requires new learning algorithms and representations, driven by theoretical insights from the social sciences. The findings of this project are disseminated in venues that target both the computational and social science communities, and the outreach and education components include building new bridges between these communities.