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- W2160360139 abstract "Analysis tools based on topic models are often used as a means to explore large amounts of unstructured data. Users often reason about the correctness of a model using relationships between words within the topics or topics within the model. We compute this useful contextual information as term co-occurrence and topic covariance and overlay it on top of standard topic model output via an intuitive interactive visualization. This is a work in progress with the end goal to combine the visual representation with interactions and online learning, so the users can directly explore (a) why a model may not align with their intuition and (b) modify the model as needed." @default.
- W2160360139 created "2016-06-24" @default.
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- W2160360139 date "2014-01-01" @default.
- W2160360139 modified "2023-10-02" @default.
- W2160360139 title "Concurrent Visualization of Relationships between Words and Topics in Topic Models" @default.
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- W2160360139 doi "https://doi.org/10.3115/v1/w14-3112" @default.
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