Papers with Hierarchical topic models
Unsupervised Hierarchical Topic Modeling via Anchor Word Clustering and Path Guidance (2024.findings-emnlp)
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| Challenge: | Existing hierarchical topic models often ignore the role of anchor words that guide text generation. |
| Approach: | They propose to use a clustering algorithm to detect anchor words that are highly consistent with every topic and add a causal path to the popular Variational Auto-Encoder framework. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling (2023.acl-long)
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| Challenge: | Existing topic models assume that topics are independent and that they are not a tree structure, which complicates the analysis. |
| Approach: | They propose a neural topic model with a Gaussian mixture prior distribution to improve the model’s ability to adapt to sparse data. |
| Outcome: | The proposed model outperforms baseline models on sparse data on a set of widely used datasets and generates more coherent topics and rational topic structures. |
Scale-Invariant Infinite Hierarchical Topic Model (2023.findings-acl)
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| Challenge: | Existing hierarchical topic models yield fragmented topics with overlapping themes whose expected probability becomes exponentially smaller along the depth of the tree. |
| Approach: | They propose a hierarchical infinite hierarchic topic model that adapts to topic creation to make expected topic probability decay considerably slower than existing models. |
| Outcome: | The proposed model has better topic uniqueness and hierarchical diversity than existing approaches. |