Papers with Lorentz model
Semantic alignment in hyperbolic space for fine-grained emotion classification (2025.acl-srw)
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| Challenge: | Existing approaches to fine-grained emotion classification operate in Euclidean space, where the flat geometry makes it difficult to distinguish semantically similar label labels. |
| Approach: | They propose a semantic alignment framework that leverages the Lorentz model of hyperbolic space to embed text and label representations into hyperbolical space via the exponential map. |
| Outcome: | The proposed framework improves on two benchmark FEC datasets. |
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 . |
Enhancing Hyperbolic Knowledge Graph Embeddings via Lorentz Transformations (2024.findings-acl)
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| Challenge: | Existing methods for knowledge graph embedding rely on tangent approximation and are not fully hyperbolic. |
| Approach: | They propose a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation. |
| Outcome: | The proposed method captures various types of relations including hierarchical structures. |
Fully Hyperbolic Neural Networks (2022.acl-long)
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| Challenge: | Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space. |
| Approach: | They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks. |
| Outcome: | The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models . |