A DOCUMENT PROCESSING SYSTEM

开发了一个C++语言程序系统，用于在Medline中查找密切相关的文档并执行文档的机器学习开始。该系统具有许多独特的功能：1）它基于许多C++类和高度模块化，因此系统中的更改相对简单。2)该系统目前通过使用C++接口从PubMed数据库中提取数据来操作PubMed数据。但是，系统接口部分的变化将使其适用于由离散文本记录组成的任何大型数据库。3)系统处理的所有数据都以倒排文件结构等形式永久存储。这些结构是可更新的，以便新数据可以在可用时不断添加到系统中。4)使用贝叶斯分析形式将文档彼此进行比较，并且从先前的文档比较中获得术语的相关性加权所基于的统计数据。这些统计信息随着每个新的处理周期而更新。该系统的最新工作涉及检索算法的最佳形式的研究。- C++语言，数据库，文档，相关度加权

A system of C++ language programs has been developed for the purpose of finding the closely related documents in Medline and for the purpose of performing machine learning onsets of documents. The system has a number of unique features: 1) It is based on a number of C++ classes and highly modular so that alterations in the system are relatively simple to perform. 2) The system currently operates PubMed data by extracting from the Sybase repositories using a C++ interface to Sybase. However, a change in the interface portion of the system would allow it to be applied to any large database consisting of discrete textual records. 3) All data processed by the system is stored in permanent form as inverted file structures, etc. These structures are updatable so that new data may be continually added to the system as it becomes available. 4) Documents are compared with each other using a Bayesian form of analysis and the statistics on which the relevance weighting of terms is based are derived from previous document comparisons. These statistics are updated with each new cycle of processing. The latest work on this system has involved a study of the optimal form for the retrieval algorithm. - C++ language, database, documents, relevance weighting of terms