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

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够的专业领域，如临床医学和分子生物学。该项目侧重于开发自引导学习技术，以快速创建特定领域的语义分析器，以及从非结构化文本中自动获取领域知识。该项目通过允许不同任务的分类器相互学习，建立了一种新的协作式自举范式，以同时学习不同任务的语义分析器。然后，这些分析器用从特定于领域的文本集合中提取的语义信息填充领域事件图。新的知识收获算法从图中获取特定领域的事实和推理规则。本项目利用兽医留言板上的帖子来探索兽医学领域，以获取真实世界的知识，用于动物健康监测。这项工作将通过开发一个新的引导框架来快速创建专门领域的语义分析工具，从而推动自然语言技术的发展。这项技术将影响许多NLP应用，包括信息提取、问题回答和摘要。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。该项目还将为动物健康监测创造新的工具，从而使社会受益，这些工具可以提供人畜共患疾病暴发（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.