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- W3100651519 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." @default.
- W3100651519 created "2020-11-23" @default.
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- W3100651519 date "2020-01-01" @default.
- W3100651519 modified "2023-10-14" @default.
- W3100651519 title "Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!" @default.
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- W3100651519 doi "https://doi.org/10.18653/v1/2020.emnlp-main.135" @default.
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