基于语义信息论和统计离群点检测的上下文信息检索
Contextual Information Retrieval based on Algorithmic Information Theory and Statistical Outlier Detection
摘要：
本文的主要贡献是设计了一个信息检索（IR）技术的基础上，数学信息论（使用归一化压缩距离- NCD），统计技术（离群值），和新的数据库结构的组织。本文展示了如何将它们集成到通用数据库中，使用长（基于文本的）查询检索信息。文中分析了两个重要问题。一方面，当文档之间的距离很低并且存在实际相似性时，如何检测“误报”。另一方面，我们提出了一种方法来构建一个文档数据库，其中相似性距离估计依赖于所选文本的长度。最后，已经进行了研究以前的问题的实验评估。
英文摘要：
The main contribution of this paper is to design an Information Retrieval (IR) technique based on Algorithmic Information Theory (using the Normalized Compression Distance- NCD), statistical techniques (outliers), and novel organization of data base structure. The paper shows how they can be integrated to retrieve information from generic databases using long (text-based) queries. Two important problems are analyzed in the paper. On the one hand, how to detect "false positives" when the distance among the documents is very low and there is actual similarity. On the other hand, we propose a way to structure a document database which similarities distance estimation depends on the length of the selected text. Finally, the experimental evaluations that have been carried out to study previous problems are shown.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
