KDI: Universal Information Access: Translingual Retrieval, Summarization, Tracking, Detection and Validation

这是一个为期三年的标准奖项。普遍信息获取项目的最终目标是通过消除--或大大降低--有效获取和使用信息的教育、语言和社会经济障碍，使信息和知识获取充分民主化。实现这一目标的进展要求我们解决以下挑战：(1)跨语言信息检索，以便跨越语言障碍和相同语言术语障碍获取文件；(2)根据用户的概况和信息需求定制的多级摘要；(3)通过高维统计学习方法进行自动分层分类；(4)检测和跟踪每个用户感兴趣的新主题和事件，以及(5)作为来源可靠性和来源间一致性的函数的信息验证。这些能力将无缝地集成到信息导航员的工作站中，使用通用的底层对象模型和以用户为中心的界面来可视化和管理信息。这些方法将根据量化指标和来自现实任务的用户反馈进行评估。普遍获取信息需要的不仅仅是搜索引擎和网络浏览。例如，许多有用的信息可能以英语以外的语言存在，或者可能来自未知的可靠性来源。此外，快速分析信息需要定制摘要、反冗余过滤器和分层组织。这些领域的进展对所有学科都是有益的，这些学科必须处理大量迅速增长的信息，如科学研究、危机管理、国际商务和改善我们的教育基础设施。拟议的研究除了对信息获取民主化产生明显影响外，还应在信息检索、机器学习、数字图书馆和以用户为中心的信息管理方面取得重大进展。

This is a three-year standard award. The ultimate goal of the Universal Information Access project is the full democratization of information and knowledge access, by removing -- or greatly lowering -- educational, linguistic and socio-economic barriers to effective information access and use. Progress towards this goal requires us to address the following challenges: (1) Translingual information retrieval, in order to access documents across language barriers, and across same-language jargon barriers, (2) Multi-level summarization, customized to the user's profile and information needs, (3) Automated hierarchical categorization, via high-dimensionality statistical learning methods, (4) Detection and tracking, of new topics and events of interest to each user as they unfold, and (5) Information validation as a function of source reliability and inter-source consistency. These capabilities will be integrated seamlessly into an information navigator's workstation, using a common underlying object model and a user-centric interface for visualization and management of information. These methods will be evaluated both with respect to quantitative metrics and with respect to user feedback from realistic tasks. Universal information access requires more than search engines and web browsing. For instance, much useful information may exist in languages other than English, or may come from sources of unknown reliability. Moreover, rapid analysis of information requires customized summarization, anti-redundancy filters, and hierarchical organization. Advances in these areas are beneficial to all disciplines which must cope with large volumes of rapidly growing information, such as scientific research, crisis management, international business, and improving our educational infrastructure. The proposed research, in addition to its clear impact on democratizing information access, should provide significant advances in: Information Retrieval, Machine Learning, Digital Libraries, and user-centered Information Management.