INFORMATION EXTRACTION AND SYNTHESIS LAB FOR TOPIC MODELING

美国国立卫生研究院（NIH）内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来执行NIH赠款的基于文本的分类，以及一个大规模的图形算法，以在空间上组织成最适合的类别的地图上显示这些数据。公众可在http://www.nihmaps.org/上获得这一互动工具。该研究所已经获得了马萨诸塞州大学信息提取与综合实验室（IESL）的服务。为了增强这一工具并进一步开发主题建模方法，该研究所将对NIH赠款进行主题建模。IESL作为主题建模的创新中心而闻名：研究贡献包括可扩展性方面的进步，例如第一个在超过10亿字的语料库上运行的模型，以及建模方面的进步，例如引入联合收割机结合主题建模方法与时间序列分析工具和回归模型的模型。该项目这一部分的具体目标是开发一个基于Java的Web应用程序来支持专家对主题的评估。

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.