ITR-(ASE+ECS)-(soc+sim+int)-Natural Language Processing Technology for Guided Study of Bioinformatics

ITR-(ASE ECS)-(soc sim int)-引导生物信息学研究的自然语言处理技术

• 批准号: 0428472
• 负责人: Dan Roth
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0428472&HistoricalAwards=false
• 关键词: ITR; ASE+ECS; soc+sim+int; Natural; Language

Recent advances in Natural Language Processing, in particular the ability to use unstructured data to answer natural language questions, are very exciting from an educational perspective. They offer the promise of systems that can automatically respond to students' questions, thus supporting not only a guided but also an open ended, exploration based, approach to learning.Developing software that supports students' learning is all about constructing the right kind of environment for students, one that facilitates rather than inhibits inquiry through a known knowledge space and provides a jumping-off space for trying to find or generate new knowledge.The goal of this project is to apply research in Computer Science -- particularly Natural Language Processing -- and the Learning Sciences, to developing an intelligent tutor that can provide this needed environment. This tutor will inhabit a human-computer interactive environment in which the computer is able to detect and track the user's cognitive and academic state and act based on this knowledge to aid the student in identifying and accessing relevant knowledge, contribute relevant factual information the student may need and guide the student in selecting potentially relevant subtasks.The testbed domain in this project involves high school and undergraduate level students studying concepts in Bioinformatics -- building on the enormous amounts of biological data and software made freely available on the Web by the Bioinformatics community, and specifically, making use of the Biology Workbench system developed at NCSA.In the context of this project, researchers will (1) develop the necessary machine learning, natural language and inference methods that can robustly support a level of natural language understanding that is sufficient to ``understand'' students and their queries well enough to direct it to the right material, make relevant suggestions and develop a meaningful dialog in the context of the subject matter; (2) create a system that is able to accommodate different student backgrounds and goals and behave appropriately, and learn as it does so; and (3) study how students learn and how to support students' learning in a computer-aided context.This project will contribute to the understanding of how students learn in a computer aided environment and use it to develop improved methods for supporting learning in these environments. This has the potential for large educational impact for large classes, distance education, and self-paced instruction. The project's computational results in areas such as natural language based human machine interaction, adaptive dialog management, user-sensitive information retrieval and extraction, and machine learning, would be widely applicable to many other domains, including intelligent information access and interactive support systems for senior citizens and other groups.

自然语言处理的最新进展，特别是使用非结构化数据回答自然语言问题的能力，从教育的角度来看是非常令人兴奋的。它们提供了可以自动回答学生问题的系统的承诺，从而不仅支持引导式学习，而且支持开放式的、基于探索的学习方法。开发支持学生学习的软件就是为学生构建正确的环境，一个促进而不是抑制通过已知的知识空间的查询，并提供一个跳跃，这个项目的目标是应用计算机科学--特别是自然语言处理--和学习科学的研究，开发一个智能导师，可以提供这种所需的环境。该导师将居住在人机交互的环境中，在该环境中，计算机能够检测和跟踪用户的认知和学术状态，并根据这些知识采取行动，以帮助学生识别和访问相关知识，提供学生可能需要的相关事实信息，并指导学生选择潜在的相关子任务。本项目的测试平台领域涉及高中和本科水平学生学习生物信息学的概念-建立在生物信息学社区在网络上免费提供的大量生物数据和软件的基础上，特别是利用NCSA开发的生物学数据库系统。在这个项目的背景下，研究人员将（1）开发必要的机器学习，自然语言和推理方法，可以强大地支持足以“理解”学生及其查询的自然语言理解水平，足以将其引导到正确的材料，提出相关建议，并在主题的背景下开展有意义的对话;（2）创建一个能够适应不同学生背景和目标的系统，并适当地表现，并在这样做的过程中学习;及（3）研究学生如何在电脑上学习及如何支援学生的学习-这个项目将有助于理解学生如何在计算机辅助环境中学习，并利用它来开发改进的方法，在这些环境中学习。 这对大班、远程教育和自定进度教学具有巨大的教育影响潜力。 该项目在基于自然语言的人机交互、自适应对话管理、用户敏感信息检索和提取以及机器学习等领域的计算结果将广泛适用于许多其他领域，包括老年人和其他群体的智能信息访问和交互式支持系统。