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没有考虑到语言如何在社会环境中变化，或者它如何依赖于说话者的态度和身份。因此，现有的语言技术很容易发生变化，特别是在非标准方言和非正式社会环境中。这个职业项目旨在通过构建能够在大型文本和语音语料库中识别社会意义的数据驱动的计算算法和表示法来缩小这一差距。这项研究的范围包括当地的社会变量，比如在谈话中用来调节礼节的称谓(“ms”，“dud”)，以及大社区群体层面的方言差异。这项研究的预期结果包括：(1)研究语言的社会功能的新的计算方法和相应的新见解；(2)利用这些见解使新的计算语言应用成为可能，并提高现有技术的稳健性；(3)计算机科学和社会科学之间更好地接触，以增强研究人员获取大规模自然产生的社会和语言数据所需的工具；以及(4)改善本科生和研究生以及广大公众对这些数据的分析和解释方面的教育。该项目使用无监督结构归纳技术来识别连接语言和社会现象的潜在结构。社会语言结构可以从各种不同的数据来源中挖掘出来，包括社交媒体和数字化人文档案。该项目旨在研究语言现象和社会现象在多个细节层面上的相互作用：(1)宏观层面的社会语言学结构，例如推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，例如调节对话中形式的一系列称谓词；(3)动态社会语言学结构，例如新词、正字法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在社会科学理论见解的推动下，采用新的学习算法和表示法。该项目的成果在面向计算和社会科学社区的场所传播，宣传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.