plkmo/Bible_Text_GCN
Pytorch implementation of "Graph Convolutional Networks for Text Classification"
This project helps religious scholars, theologians, or Bible study enthusiasts automatically categorize segments of biblical text. It takes unlabelled chapters or passages and classifies them into their correct books (e.g., Genesis, Exodus) based on patterns learned from other labelled chapters. This is particularly useful for analyzing large volumes of text where manual classification would be time-consuming.
132 stars. No commits in the last 6 months.
Use this if you need to automatically assign unlabelled biblical text segments to their correct book or section.
Not ideal if you need to classify text outside of biblical studies or require a model for very short, fragmented passages.
Stars
132
Forks
34
Language
Python
License
—
Category
Last pushed
Sep 24, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/plkmo/Bible_Text_GCN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Higher-rated alternatives
kyzhouhzau/NLPGNN
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and...
IndexFziQ/GNN4NLP-Papers
A list of recent papers about Graph Neural Network methods applied in NLP areas.
qipeng/gcn-over-pruned-trees
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (authors' PyTorch...
kenqgu/Text-GCN
A PyTorch implementation of "Graph Convolutional Networks for Text Classification." (AAAI 2019)
daiquocnguyen/Graph-Transformer
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)