math-text-semantic-networks
Python code for construction and analysis of semantic networks from text.
https://github.com/nhchristianson/math-text-semantic-networks
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
-
✓Committers with academic emails
1 of 1 committers (100.0%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Keywords
Repository
Python code for construction and analysis of semantic networks from text.
Basic Info
Statistics
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Semantic Networks in Mathematics Texts
This repository contains code associated with the paper
Nicolas H. Christianson, Ann Sizemore Blevins, and Danielle S. Bassett (2020)
Architecture and evolution of semantic networks in mathematics texts
Proc. R. Soc. A. 476: 20190741.
In particular, it contains tools for the construction and analysis of semantic networks from mathematics textbooks, as well as Jupyter notebooks detailing the production of the results included in the aforementioned paper. The methodology is designed to be broadly applicable to any text, expository or otherwise, with some small modifications. If you find this code useful in your research, please consider citing our paper; a BibTeX citation is given in CITATION.bib.
Requirements
This code is tested on Python 3.7, but may work in other versions of Python 3.
In order to do spell-checking via pyenchant, it will be necessary on non-Windows platforms to install the underlying "enchant" library, e.g. via Homebrew on Mac OS.
A few Python packages are not strictly necessary for things to work, but may be useful:
dionysus, a persistent homology package. This is not the package we generally use for calculating the persistent homology of growing semantic networks (which isripser.py), but it may be useful in conjunction with:cyclonysus, which wrapsdionysusand enables the extraction of representative cycles from persistent homology, useful for visualizing the "knowledge gaps" extracted from a growing semantic network.graph-tool, which enables construction and analysis of graphs and much more; our paper's results use its graph plotting functionality.
All other necessary packages will be installed as dependencies by pip.
Installation
Download this repository, navigate into the folder, and run pip install ..
Examples
The Notebooks folder contains as examples the Jupyter notebooks used to generate the results from our paper.
License
Shield: 
This work is licensed under a Creative Commons Attribution 4.0 International License.
Owner
- Name: Nico Christianson
- Login: nhchristianson
- Kind: user
- Location: Pasadena, CA
- Company: California Institute of Technology
- Website: nicochristianson.com
- Repositories: 1
- Profile: https://github.com/nhchristianson
PhD student, Caltech CMS
Citation (CITATION.bib)
@article{doi:10.1098/rspa.2019.0741,
author = {Christianson, Nicolas H. and Sizemore Blevins, Ann and Bassett, Danielle S.},
title = {Architecture and evolution of semantic networks in mathematics texts},
journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
volume = {476},
number = {2239},
pages = {20190741},
year = {2020},
doi = {10.1098/rspa.2019.0741},
URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2019.0741}
}
GitHub Events
Total
Last Year
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Nico Christianson | n****n@c****u | 4 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0