INFORMATION EXTRACTION AND SYNTHESIS LAB FOR TOPIC MODELING

主题建模信息提取与综合实验室

• 批准号: 7977338
• 负责人: JIM AYRES
• 金额: $ 6.08万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7977338
• 关键词: INFORMATION; EXTRACTION; SYNTHESIS; LAB; TOPIC

The National Institute of Neurological Disorders and Stroke (NINDS), an Institute within the National Institutes of Health (NIH), recently funded a research categorization tool that uses Topic Modeling to perform text-based categorization of NIH grants, and a large scale graphing algorithm to display these them on a map organized spatially into their best-fit categories. This interactive tool is available to the public at http://www.nihmaps.org/. The Institute has enlisted the services of the Information Extraction and Synthesis Laboratory (IESL) at the University of Massachusetts In order to enhance this tool and further develop the topic modeling method, the to perform topic modeling of NIH grants. IESL is well-known as a center of innovation for topic modeling: research contributions include both advances in scalability, such as the first reported model to run on a corpus of more than one billion words, and modeling advances, such as the introduction of models that combine topic modeling approaches with time series analysis tools and regression models. These technologies are showcased in the Rexa digital library and are available for public use in the MALLET toolkit.

美国国立卫生研究院 (NIH) 旗下的国家神经疾病和中风研究所 (NINDS) 最近资助了一种研究分类工具，该工具使用主题建模对 NIH 拨款进行基于文本的分类，并使用大规模绘图算法将这些拨款显示在按空间组织成最适合类别的地图上。该交互式工具可在 http://www.nihmaps.org/ 上向公众开放。该研究所已聘请马萨诸塞大学信息提取和综合实验室 (IESL) 的服务，以增强该工具并进一步开发主题建模方法，以对 NIH 拨款进行主题建模。 IESL 是众所周知的主题建模创新中心：研究贡献包括可扩展性方面的进步，例如第一个报告的在超过 10 亿个单词的语料库上运行的模型，以及建模方面的进步，例如引入将主题建模方法与时间序列分析工具和回归模型相结合的模型。这些技术在 Rexa 数字图书馆中展示，并可在 MALLET 工具包中供公众使用。