RI: Exploiting and Exploring Discourse Connectivity: Deriving New Technology and Knowledge from the Penn Discourse Treebank

RI：利用和探索话语连通性：从宾夕法尼亚大学话语树库中获取新技术和知识

• 批准号: 0705671
• 负责人: Aravind Joshi
• 金额: $ 95.5万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705671&HistoricalAwards=false
• 关键词: RI; Exploiting; Exploring; Discourse; Connectivity

Large scale corpora annotated at the sentence level have played a critical role in natural language research. They have enabled large scale integration of statistical knowledge (derived from the corpora) with linguistic knowledge leading to both technological and scientific applications, such as information extraction, question answering, summarization, and machine translation, among others. This approach is now being extended to the discourse level, thus going beyond the sentence level. Using a resource called the Penn Discourse Treebank (PDTB), a large scale corpus annotated with discourse structure along with the associated semantics, new major experimental work on discourse processing is being carried out, leading to the generation of more coherent summaries and texts, extraction of complex relations in texts, among others, as well as foundational research relevant to language technology. This work is also providing a deeper understanding of the relationship between sentence level and discourse level structures. While pursuing these goals, a variety of tools for making a productive use of the PDTB resource are also being developed. This research program is also coupled with a strong educational program involving training researchers in the PDTB methodology so that similar resources can be developed in other languages substantially divergent from English. This part of the research program has international components including collaboration with research groups in Czech Republic, India, and Finland. The international collaboration is funded by the NSF Office of International Science and Engineering.

在句子层面标注的大规模语料库在自然语言研究中起着至关重要的作用。它们实现了统计知识(来自语料库)与语言学知识的大规模整合，导致了技术和科学应用，如信息提取、问题回答、摘要和机器翻译等。这种方法现在正在扩展到语篇层面，从而超越了句子层面。利用被称为宾夕法尼亚大学语篇树库(PDTB)的大规模语料库，正在开展关于语篇处理的新的重大实验工作，导致生成更连贯的摘要和语篇，提取语篇中的复杂关系，以及与语言技术相关的基础研究。这项工作也加深了对句子层次和语篇层次结构之间关系的理解。在追求这些目标的同时，还在开发各种工具，以便有效地利用私营部门和私营部门的资源。这一研究计划还与一项强有力的教育计划相结合，该计划涉及对PDTB方法的研究人员进行培训，以便可以用与英语大相径庭的其他语言开发类似的资源。该研究计划的这一部分包含国际组成部分，包括与捷克共和国、印度和芬兰的研究小组合作。这项国际合作由美国国家科学基金会国际科学与工程办公室资助。