III: Small: Improving Information Retrieval by Analysis of Temporal Evidence in a Unified Model

III：小：通过分析统一模型中的时间证据来改进信息检索

• 批准号: 1217279
• 负责人: Miles Efron
• 金额: $ 40.88万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217279&HistoricalAwards=false
• 关键词: III; Small; Improving; Information; Retrieval

Information retrieval (IR) systems are inherently temporal. Documents change, indexes acquire new documents, and systems answer or "field" queries differently over time. The vision of this project is to capitalize on this temporality to improve the models used for predicting document relevance. The approach is based on a novel probabilistic framework to allow temporal factors to improve IR effectiveness. The framework situates temporality as a key factor in predicting the document relevance. Initial work focuses on established text retrieval settings, estimating document relevance to keyword queries. However, emerging domains such as social media and volunteer-maintained knowledge bases have an inherent temporality that demands new models. Thus, during the project, research pursues problems of filtering and topic evolution. Methods developed in this project will be experimentally evaluated using standard datasets. The project's expected outcome includes improved models and algorithms for retrieving, filtering, and organizing textual data that arrives incrementally over time. The project will benefit society in two ways. IR systems play a key role in people?s daily information use. This project will advance the public's ability to negotiate an increasingly complex information landscape, because the expected outcomes will improve search engine technology. Research results will be disseminated primarily via academic conferences and journals. Work will also be stored in an archival institutional repository, affording the public long-term access to results. Progress and general information about the project will be published on the project Web site (http://timer.lis.illinois.edu). The project will provide research experience for students and will advance scientific education. In addition, course materials will be developed to support on-line teaching of information retrieval to non-technical students.

信息检索（IR）系统是固有的时间。随着时间的推移，文档会发生变化，索引会获取新的文档，系统会以不同的方式回答或“字段”查询。该项目的愿景是利用这种时间性来改进用于预测文档相关性的模型。该方法是基于一种新的概率框架，允许时间因素，以提高IR的有效性。 该框架将时间性作为预测文档相关性的一个关键因素。 最初的工作重点是建立文本检索设置，估计文档相关的关键字查询。 然而，社交媒体和志愿人员维护的知识库等新兴领域具有内在的暂时性，需要新的模式。 因此，在项目期间，研究追求过滤和主题演变的问题。 本项目中开发的方法将使用标准数据集进行实验评估。该项目的预期成果包括改进的模型和算法，用于检索、过滤和组织随着时间的推移逐渐到达的文本数据。该项目将以两种方式造福社会。红外系统在人体中起着关键作用？的日常信息使用。 该项目将提高公众应对日益复杂的信息环境的能力，因为预期成果将改进搜索引擎技术。 研究成果将主要通过学术会议和期刊传播。 工作成果还将储存在一个机构档案库中，使公众能够长期查阅成果。 有关该项目的进展情况和一般资料将在项目网址（http：//www.example.com）上公布。timer.lis.illinois.edu 该项目将为学生提供研究经验，并将促进科学教育。此外，还将编制课程材料，以支持向非技术专业学生提供信息检索联机教学。