CI-EN-Collaborative Research: Supporting Research and Teaching for Next-Generation Search Engines in Lemur

CI-EN-协作研究：支持狐猴下一代搜索引擎的研究和教学

• 批准号: 1405829
• 负责人: W. Bruce Croft
• 金额: $ 49.96万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405829&HistoricalAwards=false
• 关键词: CI; EN; Collaborative; Research; Supporting

Many previous successes in information retrieval (IR) research have been based on open source software and test collections supported by university groups. This project will extend open-source search engine software developed by the Lemur Project to support major emerging research areas that are important for the development of the next generation of search engines that will run on mobile, speech-based platforms and be capable of learning and adapting to individual users. We will also add new capabilities that support large-scale collection of material from social media and common text mining tasks. These extensions will also be an important part of training graduate students in computer science in these critical new research areas.The impact of machine learning, natural language processing (NLP), and social media on IR research has been significant and continues to grow. Machine learning techniques, in particular, have been instrumental in helping to develop ranking functions that involve linear or non-linear combinations of many representation features of queries and documents. The enhancements we will provide in Lemur to support ongoing research in these areas include tools for training parameters, defining and incorporating new features, learning-to-rank methods that are relatively easy to use and well-integrated with the other components of the search engine, and integration with NLP and data mining toolkits. These enhancements, and the architectural changes to support multi-pass searching and flexible query processing, are not available currently in any open source search engine or IR toolkit.For further information, see the project web site at the URL: http://www.lemurproject.org/.

以前在信息检索(IR)研究方面的许多成功都是基于开放源码软件和大学团体支持的测试集。该项目将扩展由Lemur项目开发的开源搜索引擎软件，以支持主要的新兴研究领域，这些领域对开发下一代搜索引擎非常重要，这些搜索引擎将在基于语音的移动平台上运行，并能够学习和适应个人用户。我们还将添加新的功能，支持从社交媒体大规模收集材料和常见的文本挖掘任务。这些扩展也将是在这些关键的新研究领域培训计算机科学研究生的重要部分。机器学习、自然语言处理(NLP)和社交媒体对IR研究的影响一直很大，而且还在继续增长。尤其是，机器学习技术有助于开发涉及查询和文档的许多表示特征的线性或非线性组合的排序函数。我们将在Lemur中提供的增强功能，以支持这些领域正在进行的研究，包括用于训练参数的工具，定义和纳入新特征的工具，相对容易使用并与搜索引擎的其他组件良好集成的学习排名方法，以及与NLP和数据挖掘工具包的集成。这些增强功能以及支持多遍搜索和灵活查询处理的体系结构更改目前在任何开源搜索引擎或IR工具包中都不可用。有关详细信息，请参阅项目网站的网址：http://www.lemurproject.org/.