| Challenge: | Hierarchical Topic Models (HTMs) often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level subjects. |
| Approach: | They propose a Hyperbolic geometry-based Hierarchical Topic Model that incorporates hierarchical information from hyperbolic geometrics to explicitly model hierarchies in topic models. |
| Outcome: | The proposed model is significantly faster and leaves a much smaller memory footprint than the best-performing baseline. |
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CluHTM - Semantic Hierarchical Topic Modeling based on CluWords (2020.acl-main)
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| Challenge: | Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. |
| Approach: | They propose a hierarchical matrix factorization that exploits latent topics and relationships among them to create a powerful tool for data analysis and exploration. |
| Outcome: | The proposed method outperforms baselines and datasets in the vast majority of cases. |
Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)
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| Challenge: | Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics. |
| Approach: | They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents. |
| Outcome: | The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy. |
HyperText: Endowing FastText with Hyperbolic Geometry (2020.findings-emnlp)
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| Challenge: | Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters. |
| Approach: | They propose a model that uses hyperbolic geometry to model tree-like hierarchies in natural language sentences by embedding words or ngrams in hyperbolical space. |
| Outcome: | Empirically, the proposed model outperforms FastText on a range of text classification tasks with much reduced parameters. |
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
HyILR: Hyperbolic Instance-Specific Local Relationships for Hierarchical Text Classification (2025.acl-srw)
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| Challenge: | Hierarchical text classification models rely on capturing global label hierarchy, which contains static and redundant relationships. |
| Approach: | They propose a method which captures hierarchical relationships without encoding global hierarchy . they use hyperbolic geometry to model instance-specific local relationships using Lorentz model . |
| Outcome: | The proposed model captures hierarchical relationships without encoding global hierarchy . the proposed model is superior to baseline methods on four benchmark datasets . |
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification (2020.findings-emnlp)
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| Challenge: | Existing models for fine-grained entity typing have a hierarchical structure . prior work has integrated only explicit hierarchic information by formulating a hierarchy-aware loss or by representing instances and labels in a joint Euclidean embedding space. |
| Approach: | They propose a fully hyperbolic model for multi-class multi-label classification that performs all operations in hyperbolical space. |
| Outcome: | The proposed model performs all operations in hyperbolic space on two challenging datasets and shows it is comparable to state-of-the-art methods on fine-grained classification with remarkable reduction of parameter size. |
Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020.acl-main)
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| Challenge: | Existing methods for predicting missing facts do not account for hierarchical and logical patterns in KGs. |
| Approach: | They propose a class of hyperbolic KG embedding models that capture hierarchical and logical patterns. |
| Outcome: | Experimental results show that the proposed method improves by 6.1% in mean reciprocal rank in low dimensions over previous methods. |
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. |
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization (D18-1)
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| Challenge: | Existing methods for categorization of short texts use non-hierarchical flat model, but they are limited by domain-independent knowledge distribution. |
| Approach: | They propose a method which leverages hierarchical relationships between pre-defined categories to tackle the data sparsity problem. |
| Outcome: | The proposed method is competitive with the state-of-the-art methods on a multi-label categorization task for short texts using two benchmark datasets. |
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)
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| Challenge: | Automated summarization has focused on ten to twenty documents, typically news articles, but could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader. |
| Approach: | They propose a method for creating hierarchical summarization corpora from large, heterogeneous document collections by crowdsourcing relevant content and asking trained annotators to order the relevant information hierarchically. |
| Outcome: | The proposed method can be used to develop and evaluate hierarchical summarization systems. |