CompCog: Large-scale, empirically based, publicly accessible database of argument structure to support experimental and computational research

语言的基本功能是说明谁对谁做了什么。语言学家已经确定了英语将“谁”、“什么”和“谁”放入句子中的许多方式，但仍不清楚为什么不同的规则适用于不同的句子。但语言系统比这个例子所暗示的要复杂得多。英语有大约 150 种不同的方式将 who、what 和 who 放入句子中。考虑句子“Agnes receive the package from Bart”，其中句子的主语 (Agnes) gets the package。相比之下，在“艾格尼丝把包裹给了巴特”中，艾格尼丝仍然是句子的主语，但她没有得到包裹。我们也可以说“Agnes tore at the package”或“Agnes saw the package”，但“Agnes saw at the package”是行不通的。这种可变性对于语言教学和构建更强大的语音系统来说是一个问题。在这个项目中，研究人员组织了一个由公民科学家组成的大型团队，试图通过分析 6000 多个动词的语法和含义来确定英语中 who、what 和 who 的一些规则。该项目将开发和评估利用与公民科学家合作的力量的方法，促进公众更广泛地参与科学。该项目的重点是描述 VerbNet 中列出的 6340 个动词的语义，VerbNet 是最完整的动词参数结构纲要。通过 VerbNet，研究人员将为每个兼容的论证结构中的每个动词生成例句。这些句子被发布到网站 (gameswithwords.org/VerbCorner) 上，志愿者可以在网站上对这些句子进行编码，以获得 100 个语义特征，这些语义特征之前已被识别为可能与动词参数结构规则相关。该网站采用了多种策略，以使志愿者的参与更加愉快和有意义。作为该项目的一部分，研究人员将评估现有和新的分析模型，以确定何时为给定项目收集了足够的判断。鉴于众包的重要性日益增加，开发和评估这些模型应该会带来超出当前项目的红利。

A basic function of language is to say who did what to whom. Linguists have identified many of the ways English fits who, what, and whom into a sentence, but it is still unclear why different rules apply to different sentences. But the language system is far more complex than this example suggests. English has about 150 different ways of fitting who, what, and whom into a sentence. Consider the sentence "Agnes received the package from Bart" in which the subject of the sentence (Agnes) gets the package. In contrast, in "Agnes gave the package to Bart," Agnes is still the subject of the sentence but she doesn't get the package. We might also say "Agnes tore at the package" or "Agnes looked at the package" but "Agnes saw at the package" doesn't work. This variability is a problem for teaching language and for building more robust voice-enabled systems. In this project, the investigators organize a large team of citizen scientists to try to identify some of the rules of who, what, and whom in English by analyzing the grammar and meaning of over 6000 verbs. This project will develop and evaluate methods for harnessing the power of collaborations with citizen scientists, facilitating broader engagement of the public in science. This project focuses on characterizing the semantics of the 6340 verbs listed in VerbNet, the most complete compendium of verb argument structure. From VerbNet, the investigators will generate example sentences for every verb in every compatible argument structure. These sentences are posted onto a website (gameswithwords.org/VerbCorner) where volunteers can code these sentences for 100 semantic features that have been previously identified as likely being relevant to verb argument structure rules. The website incorporates a number of strategies in order to make volunteer participation more enjoyable and rewarding. As part of the project, the investigators will assess existing and new analytic models for determining when sufficient judgments have been collected for a given item. Given the increasing importance of crowd-sourcing, developing and assessing these models should provide dividends beyond the current project.