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

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。信息需求。一个术语在相关文件中出现的概率？满足信息需求的文件？它是概率信息检索理论中的一个基本量，但以往的研究对如何可靠地估计它提供了很少的线索。该项目使用探索性数据分析来识别用户指定的查询项无法匹配相关文档的常见原因，开发与每个原因相关的特征，并将它们集成到可以从数据中训练的模型中。所得到的词项必然性预测可用于最先进的检索模型，从而大大提高检索精度。术语必要性预测基于文本检索的两阶段方法。对初始检索的基于特征的分析开发出证据，这些证据可以链接到术语可能与相关文档不匹配的各种常见原因，例如中心性、同义词和抽象性。这种基于模型的方法可以从可用数据中进行训练，这使得合并新特征以测试新假设或训练特定于语料库的预测模型变得容易。它还有一个优点，即概率预测是特定于查询的，并且链接到可以指导自动术语加权以及交互式或自动查询细化的特性。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个重点干预措施。该项目通过为影响概率检索模型的中心问题提供新方法，以及对查询信息中问题的诊断和纠正，对科学界产生了影响。搜索引擎准确性的提高也会影响到大量的日常用户。提出的研究提高了？普通的人?使用非结构化关键字查询，以及经常使用复杂结构化查询来搜索结构化文档的专业搜索人员。研究结果将通过研究论文和项目网站（http://www.cs.cmu.edu/~callan/Projects/IIS-1018317/）发布。新技术将在狐猴项目的定期发布中实施和传播。s 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。

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.