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NCBITextLib

NCBITextLib is a simple but effective software library that allows one to build and access an infrastructure for large-scale text mining tasks. This library only provides basic C++ classes for building various text mining tools. Since the library provides a simple to use interface for connecting an internal text data structure to other high-level applications, it is straightforward to build ML software upon NCBITextLib. Currently, we provide three machine learning classes (naive Bayes, support vector machine and theme analysis algorithms) and example codes that use NCBITextLib.

Public Domain Notice

This work is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the authors' official duties as a United States Government employee and thus cannot be copyrighted within the United States. The data is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction.

Although all reasonable efforts have been taken to ensure the accuracy and reliability of the data and its source code, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using it. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose.

Tested System

• g++ (gcc) 4.8.1
• OS: CentOS release 6.7

How to Use

1. Download

• git clone https://github.com/ncbi-nlp/NCBITextLib.git

2. Building a library

• cd ./lib
• make

3. Compiling example programs

• cd ./applications
• make [file name], e.g. make make_db

4. Machine learing classes and examples

• cd ./applications
• BayeX.h: naive Bayes classifier (inherit from CMark)
• HubeX.h: support vector machine classifier (inherit from CMark)
• ThemX.h: theme analysis algorithm (inherit from BayeX)
• make_doc: create a Doc from samples.txt
• make_xpost: create a XPost from a Doc set (should run make_doc and make_xpost beforhand for other applications)
• run_BayeX: naive Bayes classifier example
• run_HubeX: support vector machine classifier example
• run_ThemX: theme analysis algorithm example
• find_neighbors: find neighboring documents from a seed document

NOTE: sample programs use XPost, thus should run make_doc and make_xpost beforehand.

List of Contributors

• Sun Kim
• W. John Wilbur
• Won Kim
• Donald C. Comeau
• Zhiyong Lu

Contact

Please contact sun.kim@nih.gov if you have any questions or comments.