Workshop Proposal: Content of Linguistic Annotation: Standards and Practices (CLASP)

2009年9月，在纽约市由NSF主办的一次会议上，NLP社区正在讨论人工/自动语言注释内容的标准化和协调问题。这次会议是在以前的计算研究基础设施(CRI)奖的结果的基础上，通过建立研究人员和开发人员可能遵循的标准来实现语言的全面语言注释。这些标准规范了语言内容的标记化、词性、中心语选择和其他高级标注模式共同采用的基本成分。一旦设定了标准，违规行为就应该是有意识的(而不是偶然的)，研究人员应该为任何违规行为辩护。会议还旨在建立奖励机制，以资助小型(例如学生)项目，因为几个最初符合标准的注释项目可以播下标准扎根所需的种子。智力优势：为语言注释建立一个共同的基础将：(1)使不同类型的注释更容易使用、合并和比较(来自不同的传感器、不同的手动注释集等)；(2)使一套更严格的注释标准成为可能；以及(3)促进复杂的自然语言信息应用程序的使用，这些应用程序可以同时利用几个不同项目创建的注释。广泛的影响：这种标准化过程将带来注释研究人员之间更大的合作，从而极大地提高此类研究的效率。这可以显著提高所有语言处理的技术水平，从而提高所有应用程序(自动搜索、翻译等)的水平。这依赖于对文本的自动语言分析。

At a September, 2009 NSF-sponsored meeting in New York City, the NLP community is discussing the standardization and harmonization of the content of manual/automatic linguistic annotation. The meeting is building on the results of the previous Computing Research Infrastructure (CRI) award "Towards a Comprehensive Linguistic Annotation of Language" by establishing standards that researchers and developers are likely to follow. These standards govern tokenization, part of speech, head selection and other basic componentsof linguistic content that higher level annotation schema assume in common. Once standards are set, violations should be conscious (not accidental) and researchers should justify any violations. The meeting also aims to set up incentives, in the form of grants for small (e.g., student) projects, because several initial standard-compliant annotation projects could plant the seeds needed for the standards to take root.Intellectual merit: Establishing a common base for linguistic annotation will: (1) make it easier to use, merge and compare different types of annotation (from different transducers, different manual sets of annotation, etc.); (2) make a more rigorous set of annoation standards possible; and (3) facilitate the use of sophisticated natural language informed applications that can draw on annotation created by several different projects simultaneously.Broader impact: This standardization process will bring about greater cooperation among annotation researchers and, as a result, greatly improve the efficiency of such research. This could significantly improve the state of the art of all linguistic processing, and thus, all applications (automatic search, translation, etc.) that rely on the automatic linguistic analysis of text.