-
Notifications
You must be signed in to change notification settings - Fork 6
Home
STeM is a text mining tool to help scientists and researchers evaluate new papers in their area of interest. The program was born out of a desire to easily analyze scientific papers and to help scientists or researchers to decide whether the paper is interesting or not. The analysis is based on the idea that important nouns exist in the vicinity of the selected keywords and are often used together. A contextual cross-correlation between the nouns can find appropriate new keywords. The resulting data-mining keywords are used to make a prediction about how important any new paper is. A selection of five or six papers is enough to make a prediction. However, the more papers on a topic areavailable, the more accurate the prediction will be.
Download and Installation: see section D & E
I. The first step after installing and starting the program is to update the config file. Press the config-button and update the key-list line with 3 or 4 key-words which best describe your topic. The filepath line has to be filled with the text-mining main folder. All needed subfolders will be set by STeM (see Folder structure). The pdf-to-text converter is set in the pdf2text line, the default is: pdf2text. You have also to add a preferred browser in the browser-line. Firefox is set as the default. The pdf-reader line contains the name of the pdf reader program . Evince is here the default value. If you like you can give your paper mining project a project name in the project-line. This makes it possible to keep track of different topics or projects.
II. After saving the correct config file by pressing the save-config button, press the mining button to build the first key-word mining list. Now, you are ready and set to check the relevance of individual pdf files in your collection according to these keywords. III. Copy your collection of pdf files to “your-main-folder”/check and press the “Check” button.
IV. After short time, usually below some couple of seconds, the result list will appear in the result text-field.
V. You can also use the “search web” button to do a quick literature search in the web browser, based on you text mining adjusted keyword list. Maybe you’ll find some new relevant papers for your topic.
Folder structure:
main-folder --> check = in this folder you have to place the papers you want to check --> pdf = after pressing the store-button the pdf-files will be copied here and deleted from the checked folder. --> texts = after converting pdf-files to text-files the text-files will be stored here for mining
WARNING: papers are deleted permanently from the check folder by using the delete-button. Therefore, do only copy papers into the check folder and keep your library in another directory.
STeM was built during a feasibility study I made to see whether it is possible to evaluate scientific papers by nouns or not. I used papers out of my area of knowledge (receiver coupling to the seafloor, microseismic and seismic on ice) and compared the text-mining results with my personal assessment of the articles. I got a good agreement between my assessment and the text-mining results. This promising result helped to focus only on the interesting papers without reading them first. I even found some papers I did not find before, by using the “search web”-button.. Since, I am no expert in all areas ;-) I would very much appreciate your feedback to this little program and how helpful you deem it to be for your area of interest.
Please share your experiences with me and others via [email protected] or GitHub.
STeM
License: Copyright (C) 2018 Marcus Landschulze
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/
The text-mining functions are built on the Python NLTK API from the NLTK project.
License: Copyright (C) 2001-2017 NLTK Project
Licensed under the Apache License, Version 2.0 (the 'License'); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either expressed or implied. See the License for the specific language governing permissions and limitations under the License.
Download page: https://github.com/malares/STeM-Scientifc-Paper-Mining-Tool
follow the instructions on GitHub
STeM is written in Python3 and should run on all platforms, but I have only tested it on Linux Ubuntu and Windows 8. However, for Windows machines I could not find a free available command-line based pdf-to-text converter, so you can use Acrobat reader to manually convert the .pdf files to .txt and use the text files instead.
Requirements:
Python 3.5 or higher Python3-tk PDF-reader such as Evince or Acrobat pdf2text converter
Installation on a Linux machine:
I. Click on the SteM-x_all.deb in your install folder and follow the instructions. II. Run stem from the terminal.
Installation on a Windows machine:
I. Run the execute file: SteM-x-x86_64.exe
Work-around for Windows, when you have no admin-rights:
I. Unpack the SteM-x.tar.gz in a folder with install rights. II. Open the file mdm_config.py in a text-editor and change the following: delete or comment out the line: path = „usr/local/etc“ line and uncomment the line: path = os.getcwd() . Save the mdm_config.py file III. Run pyhton3 stem.
Installed files:
stem /usr/local/bin main python script
stem_config.cfg /usr/local/etc main config file
stem_readme /usr/local/etc help text-file