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.
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
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abstract
Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our approach performs as well as classical topic models, but with lower runtime and computational complexity.
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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.