RI: Small: Acquiring Domain Knowledge from Text through Cooperative Bootstrapping

RI：小型：通过协作引导从文本中获取领域知识

• 批准号: 1018314
• 负责人: Ellen Riloff
• 金额: $ 38.38万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018314&HistoricalAwards=false
• 关键词: RI; Small; Acquiring; Domain; Knowledge

Some of the most pressing needs for natural language processing (NLP) technology come from specialized domains where broad-coverage solutions are not sufficient, such as clinical medicine and molecular biology. This project focuses on the development of bootstrapped learning techniques to rapidly create domain-specific semantic analyzers, and the automatic harvesting of domain knowledge from unstructured text.This project establishes a new cooperative bootstrapping paradigm to learn semantic analyzers for different tasks simultaneously by allowing classifiers for different tasks to learn from each other.These analyzers then populate a domain event graph with semantic information extracted from a domain-specific text collection. New knowledge harvesting algorithms acquire domain-specific facts and inference rules from the graph. This project explores the domain of veterinary medicine using message board posts by veterinarians to acquire real-world knowledge for the purposes of animal health surveillance.This work will advance the state-of-the-art in natural language technology by developing a new bootstrapping framework to rapidly create semantic analysis tools for specialized domains. This technology will impact many NLP applications, including information extraction, question answering, and summarization. The knowledge harvesting tools will be made publicly available to allow for direct impact across many disciplines that have a need to extract knowledge from unstructured text collections. This project will also benefit society by creating new tools for animal health surveillance, which could provide early warning signs of zoonotic disease outbreaks (such as bird flu and mad cow disease), exposures to toxic substances, and contamination in the food chain.

对自然语言处理 (NLP) 技术的一些最迫切的需求来自于广泛覆盖的解决方案不足以满足的专业领域，例如临床医学和分子生物学。 该项目专注于开发引导学习技术，以快速创建特定于领域的语义分析器，以及从非结构化文本中自动收获领域知识。该项目建立了一种新的协作引导范式，通过允许不同任务的分类器相互学习，同时学习不同任务的语义分析器。然后，这些分析器使用从非结构化文本中提取的语义信息填充领域事件图。 特定领域的文本集合。新的知识收集算法从图中获取特定领域的事实和推理规则。该项目利用兽医的留言板帖子来探索兽医医学领域，以获取用于动物健康监测的现实世界知识。这项工作将通过开发新的引导框架来快速创建专门领域的语义分析工具，从而推进自然语言技术的最先进水平。 这项技术将影响许多 NLP 应用，包括信息提取、问答和摘要。 知识收集工具将公开提供，以便对需要从非结构化文本集合中提取知识的许多学科产生直接影响。 该项目还将通过创建新的动物健康监测工具来造福社会，该工具可以提供人畜共患疾病爆发（例如禽流感和疯牛病）、有毒物质暴露和食物链污染的早期预警信号。