Lower Dimensional Representation of Text Data for Efficient and Effective Information Retrieval

用于高效且有效的信息检索的文本数据的低维表示

• 批准号: 0549253
• 负责人: Haesun Park
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0549253&HistoricalAwards=false
• 关键词: Lower; Dimensional; Representation; Text; Data

ABSTRACT 0204109Haesun ParkUniversity of Minnesota- Twin CitiesDue to today's exponential growth of the internet and computing power, an information retrievalsystem is expected to handle a tremendous amount of data, and users demand more efficient techniquesto obtain useful information from the flood of data. The goal of this proposed research is to find lower dimensional representations of text data in vector space based information retrieval. Dimension reduction is imperative for achieving high efficiency and effectiveness in manipulating the massive quantity of data in today's information retrieval system. A problem of fundamental importance here is to achieve better representation of the data with relatively severe dimension reduction, rather than simple dimension reduction through a lower rank approximation of a matrix. One difficulty is that it is not easy to measure by a theoretical formula how well a certain dimension reduction method provides a good representation of the original data, and it will be essential to conduct theoretical research in parallel with experimental study.

摘要0204109Haesun Park明尼苏达大学双城分校由于当今互联网和计算能力的指数增长，信息检索系统预计将处理大量的数据，用户需要更有效的技术来从大量的数据中获取有用的信息。 本研究的目标是在基于向量空间的信息检索中找到文本数据的低维表示。在当今的信息检索系统中，降维是实现高效率和高效率地处理大量数据的必要条件。这里的一个根本重要性的问题是实现更好的表示的数据与相对严重的降维，而不是简单的降维通过一个矩阵的低秩近似。一个困难是，它是不容易衡量的理论公式，以及某种降维方法提供了一个良好的表示原始数据，这将是必要的，进行理论研究与实验研究并行。