A Document Processing System

为了在Medline中找到密切相关的文档，并为了在文档集上执行机器学习的目的，开发了一个c++语言程序系统。该系统有许多独特的特点：1)它基于许多c++类，并且高度模块化，因此对系统的更改相对容易执行。2)系统目前处理PubMed数据的方式是从Sybase存储库中提取数据，使用一个c++的Sybase接口。但是，对系统的接口部分进行更改将使其能够应用于由离散文本记录组成的任何大型数据库。3)系统处理的数据以压缩文件结构等形式存储。这些结构是可更新的，因此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对文件进行相互比较。5)该系统的最新工作涉及添加使用EM算法方法生成主题的能力。最近还增加了多线程和内存映射功能，以加快处理速度。

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 has been used for mining email communications for the NLM help desk.