A Document Processing System

一个系统的C++语言程序已经开发的目的是找到密切相关的文件在Medline和执行机器学习的文件集的目的。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处理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)由系统处理的数据被存储为压缩文件结构等。这些结构是可更新的，使得新数据可以在其变得可用时被持续地添加到系统。4)使用贝叶斯分析形式对文档进行相互比较。5)该系统的最新工作涉及使用EM算法方法添加生成主题的能力。此外，最近的代码已经多线程和内存映射功能，以加快处理。 这里描述的系统现在不仅用于处理我们研究目的的所有MEDLINE，而且还用于为NLM内外的其他小组的任意文本生成相关文档。该系统目前被证明是有用的，在测试不同的检索参数和方法的PubMedHealth记录。

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 on sets 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 processes 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) Data processed by the system is stored as compressed 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. 5) The latest work on this system has involved adding the ability to generate themes using an EM algorithm approach. Also recently code has been multithreaded and memory mapping capabilities added to speed up processing. The system described here is now not only being used to process all of MEDLINE for our research purposes, but also to produce the related documents for arbitrary pieces of text by other groups here in the NLM and outside of the NLM. The system is currently proving useful in testing different retrieval parameters and methods on the PubMedHealth records.