Tree-Structured Neural Topic Model
Masaru Isonuma, Junichiro Mori, Danushka Bollegala, Ichiro Sakata
Abstract
This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches. Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks. With the help of autoencoding variational Bayes, our model improves data scalability and achieves competitive performance when inducing latent topics and tree structures, as compared to a prior tree-structured topic model This work extends the tree-structured topic model such that it can be incorporated with neural models for downstream tasks.
Topics & Concepts
Tree (set theory)Computer scienceDecision tree modelScalabilityArtificial intelligenceArtificial neural networkMachine learningTree structureData miningDecision treeAlgorithmBinary treeMathematicsMathematical analysisDatabaseTopic ModelingDomain Adaptation and Few-Shot LearningNatural Language Processing Techniques