III: Small: Modeling and Predicting Term Mismatch for Full-Text Retrieval

III：小：全文检索的术语不匹配建模和预测

• 批准号: 1018317
• 负责人: Jamie Callan
• 金额: $ 49.55万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018317&HistoricalAwards=false
• 关键词: III; Small; Modeling; Predicting; Term

Many text search engines use probabilistic reasoning to determine how well a word represents a person?s information need. The probability that a term appears in relevant documents ? documents that satisfy the information need ? is a fundamental quantity in the theory of probabilistic information retrieval, however prior research provided few clues about how to estimate it reliably. This project uses exploratory data analysis to identify common reasons that user-specified query terms fail to match relevant documents, develops features correlated with each reason, and integrates them into a model that can be trained from data. The resulting term necessity predictions can be used in state-of-the-art retrieval models to improve retrieval accuracy substantially.Term necessity predictions are based on a two-stage approach to text retrieval. A feature-based analysis of an initial retrieval develops evidence that can be linked to a variety of common reasons that a term might not match relevant documents, for example, centrality, synonymy, and abstractness. This model-based approach can be trained from available data, making it easy to incorporate new features that test new hypotheses, or to train a corpus-specific predictive model. It also has the advantage that probability predictions are query-specific, and linked to features that can guide automatic term weighting as well as interactive or automatic query refinement. The project develops several focused interventions for interactive, automatic query expansion, and relevance feedback refinement of queries.This project makes an impact on the scientific community by providing new approaches to a central problem that affects probabilistic retrieval models, and the diagnosis and correction of problems in query formation. Improvements in search engine accuracy also affect a broad population of everyday users. The proposed research improves search accuracy for ?ordinary people? using unstructured keyword queries, as well as professional searchers who often use sophisticated structured queries to search structured documents.Research results will be disseminated in research papers and via project web site (http://www.cs.cmu.edu/~callan/Projects/IIS-1018317/). New techniques will be implemented and disseminated in periodic releases of the Lemur Project?s Indri search engine (http://www.lemurproject.org/indri/). Indri is used by a broad international research community, thus this form of dissemination makes it more likely that other researchers will study and extend the proposed research.

许多文本搜索引擎使用概率推理来确定一个词代表一个人的程度。的信息需求。 一个术语出现在相关文档中的概率？满足信息需求的文件？是概率信息检索理论中的一个基本量，然而先前的研究提供了一些关于如何可靠地估计它的线索。 该项目使用探索性数据分析来识别用户指定的查询词无法匹配相关文档的常见原因，开发与每个原因相关的功能，并将其集成到可以从数据中训练的模型中。 所得到的术语必要性预测可以用于最先进的检索模型，以提高检索精度显着。术语必要性预测是基于一个两阶段的方法来文本检索。 对初始检索的基于特征的分析开发了证据，这些证据可以与术语可能与相关文档不匹配的各种常见原因相关联，例如中心性，同义性和抽象性。 这种基于模型的方法可以从可用的数据中进行训练，从而可以轻松地整合测试新假设的新功能，或者训练特定于语料库的预测模型。 它还有一个优点，即概率预测是特定于查询的，并且与可以指导自动术语加权以及交互式或自动查询细化的功能相关联。 该项目为交互式、自动查询扩展和查询的相关反馈细化开发了几种重点干预措施，该项目通过提供新的方法来解决影响概率检索模型的核心问题，以及诊断和纠正查询形成中的问题，从而对科学界产生影响。 搜索引擎准确性的提高也会影响到广大的日常用户。 建议的研究提高搜索精度？普通人吗研究结果将以研究论文的形式和通过项目网站（http：//www.cs.cmu.edu/projects/IIS-1018317/）传播。 新技术将在狐猴项目的定期发布中实施和传播？的Indri搜索引擎（http：//www.lemurproject.org/indri/）。 Indri被广泛的国际研究界使用，因此这种传播形式使其他研究人员更有可能研究和扩展拟议的研究。