III-COR: Searching Archives of Community Knowledge

III-COR：搜索社区知识档案

• 批准号: 0711348
• 负责人: W. Bruce Croft
• 金额: --
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0711348&HistoricalAwards=false
• 关键词: III; COR; Searching; Archives; Community

Croft, W. BruceUniversity of Massachusetts AmherstIII-COR Searching Archives of Community KnowledgeThe problem of recognizing or finding questions has received little attention, but is an important problem that is conceptually different than typical document search. The use of text transformation models learned from the Q&A archives has the potential of considerably advancing our knowledge of how models that have been very successful in machine translation applications can be applied to improve the effectiveness of search. This project will use two large archives of questions and answers to develop, test, and compare algorithms for more effective retrieval of questions that are semantically similar to the question posed by a current user and look to retrieve answers with a high probability of relevance. One aspect of addressing this challenge will be to apply text transformation techniques developed to support machine translation to the Q&A archives in order to learn probabilities to associate with word substitution in identifying semantically similar queries. The techniques will be evaluated for both English and Korean (since one of the Q&A archives is in Korean). Over a dozen alternative approaches to developing similarity measures will be tried and compared using a variety of performance metrics, primarily metrics that focus on the precision/relevance of results returned at the top of a ranked list. Development of successful approaches to the problem of retrieval of appropriate answers from archives of questions and answers will benefit many application areas

Croft，W.马萨诸塞州的布鲁斯大学AmherstIII-COR搜索社区知识档案识别或发现问题的问题很少受到关注，但它是一个重要的问题，在概念上不同于典型的文档搜索。从问答档案中学习的文本转换模型的使用有可能大大提高我们对机器翻译应用中非常成功的模型如何应用于提高搜索效率的认识。 该项目将使用两个大型的问题和答案档案来开发、测试和比较算法，以更有效地检索与当前用户提出的问题在语义上相似的问题，并寻找具有高相关性的答案。解决这一挑战的一个方面将是应用开发用于支持机器翻译的文本转换技术到问答档案，以便学习概率以在识别语义相似的查询时与单词替换相关联。将对英语和韩语的技术进行评价（因为其中一个问答档案是韩语的）。将尝试十几种开发相似性度量的替代方法，并使用各种性能指标进行比较，主要是关注排名列表顶部返回的结果的精度/相关性的指标。开发成功的方法来解决从问答档案中检索适当答案的问题将使许多应用领域受益