A document clustering pipeline that builds a named-entity similarity graph, then runs graph convolutional clustering on LLM embeddings, outperforms co-occurrence and KNN graph baselines in the paper's experiments.
k-means++: The advantages of careful seeding
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Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering
A document clustering pipeline that builds a named-entity similarity graph, then runs graph convolutional clustering on LLM embeddings, outperforms co-occurrence and KNN graph baselines in the paper's experiments.