III: Small: Transforming Long Queries

III：小：转换长查询

• 批准号: 0914442
• 负责人: W. Bruce Croft
• 金额: $ 49.98万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914442&HistoricalAwards=false
• 关键词: III; Small; Transforming; Long; Queries

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Many information needs can be more easily expressed using longer, sentence-length queries, but the inadequacies of current search engines force people to try to think up the right combination of keywords to find relevant documents. This can be very difficult and often leads to search failures. On the other hand, long queries are handled poorly by current search engines. The focus of this project is on developing retrieval algorithms and query processing techniques that will significantly improve the effectiveness of long queries. A specific emphasis is on techniques for transforming long queries into semantically equivalent queries that produce better search results. In contrast to purely linguistic approaches to paraphrasing, query transformation is done in the context of, and guided by, retrieval models. Query transformation steps such as stemming, segmentation, and expansion have been studied for many years, and we are both extending and integrating this work in a common framework. The new query processing techniques for long queries are being developed and distributed using the NSF-funded Lemur toolkit from UMass/CMU, and are being evaluated using a variety of document and query collections from sources such as the web, social media sites such as forums, and TREC, with an involvement of graduate and undergraduate students. The project Web site (http://ciir.cs.umass.edu/research/longqueries) will be used to further disseminate results. Given that search is one of the two most common activities on the web and that new modalities for search, such as voice interfaces and collaborative question answering, are increasing the importance of long queries, this research could have a very broad impact, both in the home and the office.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。许多信息需求可以更容易地使用更长的，更长的查询来表达，但当前搜索引擎的不足之处迫使人们试图想出正确的关键字组合来找到相关文件。 这可能非常困难，并且经常导致搜索失败。 另一方面，目前的搜索引擎对长查询处理得很差。这个项目的重点是开发检索算法和查询处理技术，这将显着提高长查询的有效性。一个具体的重点是将长查询转换为语义等价的查询，产生更好的搜索结果的技术。与纯语言学的释义方法不同，查询转换是在检索模型的上下文中完成的，并由检索模型指导。查询转换的步骤，如词干提取，分割和扩展已经研究了很多年，我们都在一个共同的框架中扩展和集成这项工作。长查询的新查询处理技术正在开发和分发使用NSF资助的Lemur工具包从马萨诸塞大学/CMU，并正在评估使用各种文档和查询集合的来源，如网络，社交媒体网站，如论坛，和TREC，与研究生和本科生的参与。将利用项目网址（http：//ciir.cs.umass.edu/research/longqueries）进一步传播成果。考虑到搜索是网络上最常见的两种活动之一，以及语音界面和协作问答等新的搜索模式正在增加长查询的重要性，这项研究可能会在家庭和办公室产生非常广泛的影响。