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

自然语言处理(NLP)技术的一些最紧迫的需求来自一些专业领域，在这些领域，广泛的解决方案是不够的，如临床医学和分子生物学。该项目致力于开发自举学习技术来快速创建特定领域的语义分析器，并从非结构化文本中自动获取领域知识。该项目建立了一个新的协作自举范例，通过允许不同任务的分类器相互学习来同时学习不同任务的语义分析器。这些分析器然后用从特定领域文本集合中提取的语义信息填充领域事件图。新的知识获取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探索兽医领域，以动物健康监测为目的获取真实世界的知识。这项工作将通过开发一个新的自举框架来快速创建专门领域的语义分析工具，从而推动自然语言技术的发展。这项技术将影响许多自然语言处理应用，包括信息提取、问答和摘要。知识收集工具将公之于众，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。该项目还将通过创造动物健康监测的新工具来造福社会，这些工具可以提供人畜共患病爆发(如禽流感和疯牛病)、接触有毒物质和食物链污染的早期预警信号。

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.