Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion (2025.findings-acl)
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| Challenge: | Existing taxonomy expansion methods struggle with representation limits and generalization, while generative methods process all candidates at once, introducing noise and exceeding context limits. |
| Approach: | They propose a plug-and-play framework that combines discriminative ranking and generative reasoning for efficient taxonomy expansion. |
| Outcome: | Experiments show that LORex improves accuracy by 12% and similarity by 5% over state-of-the-art methods. |
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TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (2021.emnlp-main)
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| Challenge: | Existing taxonomies are unable to maintain coverage due to the rising of new concepts . TEMP uses pre-trained contextual encoders to predict the position of new ideas . |
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SynET: Synonym Expansion using Transitivity (2020.findings-emnlp)
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