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

语言的一个基本功能是说谁对谁做了什么。语言学家已经确定了英语将who， what和whom放入一个句子中的许多方式，但仍然不清楚为什么不同的规则适用于不同的句子。但语言系统远比这个例子所显示的要复杂得多。英语中有大约150种不同的方式来把who， what和whom放在一个句子中。考虑句子“Agnes received the package from Bart”，其中句子的主语（Agnes）收到了包裹。相反，在“Agnes gave the package to Bart”中，Agnes仍然是句子的主语，但她没有得到包裹。我们也可以说“阿格尼斯撕了包装”或者“阿格尼斯看了包装”但是“阿格尼斯看了包装”是行不通的。这种可变性对于语言教学和构建更健壮的语音支持系统来说是一个问题。在这个项目中，调查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whom的一些规则。这个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。这个项目的重点是描述动词参数结构最完整的纲要vernet中列出的6340个动词的语义特征。从动词网中，研究者将为每个兼容的论点结构中的每个动词生成例句。这些句子被发布在一个网站（gameswithwords.org/VerbCorner）上，志愿者可以为这些句子编码100个语义特征，这些特征之前已经被识别为可能与动词论点结构规则相关。该网站采用了一些策略，以使志愿者参与更愉快和有益。作为该项目的一部分，调查人员将评估现有的和新的分析模型，以确定何时对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提供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.