CRI - Towards a Comprehensive Linguistic Annotation of Language

这个项目开发了一个统一的语言注释(ULA)，它在一个框架中整合了不同层次的注释(例如，语义、话语、时间、观点)，提供了一个具有平衡和注释数据的大型词库。这项工作涉及整合几个现有的资源，包括PropBank、NomBank、TimeBank、Penn Discount Treebank以及相互参考和意见注释。这项工作解决了在自动生成语义表示方面缺乏进展的问题，这是目前自然语言处理的主要障碍。该项目旨在就元规范框架达成国际共识，允许不同模式的个别注释彼此并存(一致性)，并使来自不同模式的规范组件引用合并的信息(整合)；-改进为可靠类型的语义注释产生高性能系统的技术；-为统一的语言注释过程产生稳定和独立于语言的方法，并配有可广泛使用和广泛适用的工具和准则；-通过纳入更多的注释模式、新的体裁和更多的语言，(与讲习班一起)验证《统一语言法》的通用性和稳健性；在自然语言处理(NLP)系统中纳入自动标记器，预计将通过将自然语言处理(NLP)转移到更丰富、更深入的处理的新水平，从而提高问题回答、信息提取和机器翻译等方面的性能。这些标注数据为自动标记器提供了训练材料，并为进一步的语料库语言学研究提供了丰富的数据。这个项目应该让我们离理解意义的本质更近一步。更广泛的影响：该项目提供了产生大量文本的抽象表示的机会，因此产生了大量的知识，这可能是将一般知识表示变成更容易处理的问题的关键。该活动加强了研究和教育的基础设施，提供了一种资源，可以导致在健壮、覆盖面广的语义处理方面取得重大进展。工作坊为学生提供了大量的学习机会。

This project, developing a Unified Linguistic Annotation (ULA) that integrates in one framework different layers of annotation (e.g., semantics, discourse, temporal, opinions), provides a large word corpus with balanced and annotated data. The effort involves the integration of several existing resources, including PropBank, NomBank, TimeBank, Penn Discourse Treebank, and coreference and opinion annotations. The work addresses the lack of progress in automatically producing semantic representations, a current major obstacle for natural language processing. The project aims at-Achieving an international consensus on a meta-specification framework allowing individual annotations to cohabit with one another (consistency) and specification components from different schemas to refer to merged information (integration);-Improving techniques for producing high performing systems for the reliable types of semantic annotation;-Producing a stable and language-independent methodology for the process of unified linguistic annotation, complete with widely accessible and broadly applicable tools and guidelines;-Validating (with workshops) generality and robustness of ULA by incorporating additional annotation schemas, new genres, and additional languages; and-Actively promoting dissemination of the techniques embodied in the ULA, as well as the resulting annotated corpora throughout the community, for use and further evaluation.The incorporation of automatic taggers into natural language processing (NLP) systems is expected to improve performance in question answering, information extraction, and machine translation, among others, by moving NLP to a new level of richer, deeper processing. The annotated data provides training material for automatic taggers and a wealth of data for further corpus linguistic studies. This project should bring us one step closer to understanding the nature of meaning.Broader Impact: The project offers the opportunity to produce an abstract representation of a vast amount of text, and therefore a vast amount of knowledge, which may be key to turning knowledge representation in general into a more tractable problem. The activity enhances infrastructure for research and education by providing a resource that could lead to major advances in robust, broad coverage semantic processing. The workshops provide tremendous learning opportunities for the students.