Corpora of Non-Linguistic Symbol Systems, and Statistical Analysis

非语言符号系统语料库和统计分析

• 批准号: 1049308
• 负责人: Steven Bedrick
• 金额: $ 11.41万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049308&HistoricalAwards=false
• 关键词: Corpora; Non; Linguistic; Symbol; Systems

Throughout the millenia, humans have used graphical symbols. 5000 years ago, in Mesopotamia, a system of symbols was codified into what was to become the world's first writing system. But in addition to writing systems, many symbol systems have been developed that, though they do communicate information, do not encode language. Such non-linguistic symbol systems include familiar examples such as mathematical symbology, European heraldry, or scouting merit badges, as well as less familiar ones such as Mesopotamian deity symbols or Dakota winter counts. Like writing, such systems are an important part of the cultures that created them.Now suppose one has a symbol system whose interpretation is unknown. Short of deciphering it as a writing system, or otherwise providing a rigorous testable interpretation, are there any methods that can determine whether one is dealing with writing or a non-linguistic system? Some recent high-profile papers have indeed claimed to provide statistical evidence that a couple of ancient systems were linguistic. But, one problem with that work is that in order to determine which of two categories an unknown system belongs to, it is very useful to have a large set of examples of the two categories in question. In the case of writing systems, we now have many electronic corpora from hundreds of languages, both ancient and modern. But electronic corpora of non-linguistic systems are few.This project will fill this void by developing and releasing to the public electronic corpora of a range of non-linguistic systems, including those named above as well as several others. It will also investigate statistical and machine learning methods that might help in distinguishing written language from other graphical communication. And this in turn will lead to a better understanding of a fundamental question about humanity: what sets language apart from other forms of communication?

在整个千年中，人类都使用了图形符号。 5000年前，在美索不达米亚，一个符号系统被编纂为世界上第一个写作系统。但是，除了写作系统外，已经开发了许多符号系统，尽管它们确实传达了信息，但仍未编码语言。这样的非语言符号系统包括熟悉的示例，例如数学符号，欧洲纹章或侦察功绩徽章，以及诸如美索不达米亚神灵符号或达科他州冬季计数之类的较不熟悉的徽章。 像写作一样，这种系统是创造它们的文化的重要组成部分。现在假设一个人的解释是未知的符号系统。缺乏将其解密为写作系统，或者提供严格的可测试解释，是否有任何方法可以确定一个人是在处理写作还是非语言系统？最近一些备受瞩目的论文确实声称提供了统计证据，表明几个古代系统是语言上的。但是，这项工作的一个问题是，为了确定未知系统所属的两个类别中的哪个中的哪一个，有两个类别的大量示例非常有用。在写作系统的情况下，我们现在拥有数百种古代和现代语言的电子语料库。但是，非语言系统的电子语料库很少。该项目将通过开发和发布一系列非语言系统（包括上面命名的系统以及其他几个）来填补这一空白。 它还将研究统计和机器学习方法，这些方法可能有助于将书面语言与其他图形交流区分开。反过来，这将使人们更好地理解有关人类的基本问题：是什么使语言与其他形式的交流不同？