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: 1405045
• 负责人: Jamie Callan
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 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=1405045&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/.

以前许多成功的信息检索（IR）研究都是基于开源软件和由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，这些领域对下一代搜索引擎的开发至关重要，这些搜索引擎将运行在移动、基于语音的平台上，并能够学习和适应个人用户。我们还将添加支持从社交媒体和常见文本挖掘任务中大规模收集材料的新功能。这些扩展也将是在这些关键的新研究领域培养计算机科学研究生的重要组成部分。机器学习、自然语言处理（NLP）和社交媒体对IR研究的影响一直很显著，而且还在继续增长。特别是机器学习技术，在帮助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方面发挥了重要作用。我们将在Lemur中提供增强功能，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、定义和合并新功能的工具、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序方法，以及与NLP和数据挖掘工具包的集成。这些增强以及支持多通道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工具包中都不提供。欲了解更多信息，请参阅项目网站的URL: http://www.lemurproject.org/。