INFORMATION EXTRACTION AND SYNTHESIS LAB FOR TOPIC MODELING

美国国立卫生研究院(NIH)下属的国家神经疾病和中风研究所(NINDS)最近资助了一个研究分类工具，该工具使用主题建模对NIH的拨款进行基于文本的分类，并使用大规模图表算法将这些资金显示在空间组织成最佳类别的地图上。该交互式工具可在http://www.nihmaps.org/.上向公众提供该研究所聘请了马萨诸塞大学信息提取和综合实验室(IESL)的服务，以加强这一工具，并进一步开发主题建模方法，即国家卫生研究院赠款的主题建模。IESL是众所周知的主题建模创新中心：研究贡献包括可伸缩性方面的进展，例如第一个报告的在超过10亿字的语料库上运行的模型，以及建模方面的进步，例如引入了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。该项目这一部分的具体目标是开发一个基于Java的网络应用程序，以支持专家对专题的评估。

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. The specific goal of this portion of the project is to develop a Java-based web application to support evaluation of topics by experts.