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- W4300746616 abstract "The anchor words algorithm performs provably efficient topic model inference by finding an approximate convex hull in a high-dimensional word co-occurrence space. However, the existing greedy algorithm often selects poor anchor words, reducing topic quality and interpretability. Rather than finding an approximate convex hull in a high-dimensional space, we propose to find an exact convex hull in a visualizable 2- or 3-dimensional space. Such low-dimensional embeddings both improve topics and clearly show users why the algorithm selects certain words." @default.
- W4300746616 created "2022-10-04" @default.
- W4300746616 creator A5066049461 @default.
- W4300746616 creator A5086934220 @default.
- W4300746616 date "2017-11-18" @default.
- W4300746616 modified "2023-09-25" @default.
- W4300746616 title "Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference" @default.
- W4300746616 doi "https://doi.org/10.48550/arxiv.1711.06826" @default.
- W4300746616 hasPublicationYear "2017" @default.
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