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• W2788689269 abstract "Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of held-out document classification tasks." @default.
• W2788689269 created "2018-03-06" @default.
• W2788689269 creator A5057799988 @default.
• W2788689269 date "2018-02-22" @default.
• W2788689269 modified "2023-10-01" @default.
• W2788689269 title "Learning Topic Models by Neighborhood Aggregation" @default.
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• W2788689269 hasPublicationYear "2018" @default.
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