XufengXufengXufeng/try_gcn
try different opts on word context graph with GCN and GAT to obtain word embeddings.
This project helps natural language processing practitioners explore different ways to create numerical representations (embeddings) of words. It takes your word context data and applies graph neural networks to generate word embeddings. This is for researchers or engineers working on tasks like text analysis or information retrieval who want to experiment with advanced embedding techniques.
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Use this if you are interested in comparing how Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) perform against traditional methods like Node2Vec for generating word embeddings from context graphs.
Not ideal if you are looking for an out-of-the-box, production-ready solution for standard word embedding generation, as this project is more for experimental comparison.
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Mar 26, 2020
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