Generative Topic Embedding: a Continuous Representation of Documents
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Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensi…
Document ClassificationVariational InferenceThe Dynamic Embedded Topic Model
Topic modeling analyzes documents to learn meaningful patterns of words. For documents collected in sequence, dynamic topic models capture how these patterns vary over time. We develop the dynamic embedded topic model (D…
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Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents. On the other hand, topic models provid…
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A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is focused on capturing the word co-occurre…
parameter estimationWord EmbeddingsGraph2topic: an opensource topic modeling framework based on sentence embedding and community detection
It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. Ho…
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