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Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs).
Hyperbolic groups
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Poincaré embeddings for learning hierarchical representations
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Hyperbolic entailment cones for learning hierarchical embeddings
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Damion M Dooley, Emma J Griffiths, Gurinder S Gosal, Pier L Buttigieg, Robert Hoehndorf, Matthew C Lange, Lynn M Schriml, Fiona SL Brinkman, and William WL Hsiao · 2018
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bert-as-service
Han Xiao · 2018
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Hyperbolic neural networks
Octavian Ganea, Gary Bécigneul, and Thomas Hofmann · 2018
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Bhuwan Dhingra, Christopher Shallue, Mohammad Norouzi, Andrew Dai, and George Dahl · 2018
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Ilya Loshchilov and Frank Hutter · 2018
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Analyzing bert’s knowledge of hypernymy via prompting
Michael Hanna and David Mareček · 2021
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Self-alignment pretraining for biomedical entity representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, and Nigel Collier · 2021
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Owl2vec*: Embedding of owl ontologies
Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, and Ian Horrocks · 2021
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Does bert know that the is-a relation is transitive?
Ruixi Lin and Hwee Tou Ng · 2022
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Concept placement using bert trained by transforming and summarizing biomedical ontology structure
Hao Liu, Yehoshua Perl, and James Geller · 2020
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Masked language model scoring
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Probing bert in hyperbolic spaces
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OpenAI · 2023
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Language model analysis for ontology subsumption inference
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Contextual semantic embeddings for ontology subsumption prediction
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Deeponto: A python package for ontology engineering with deep learning
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