HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings (2022.lrec-1)
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| Challenge: | Existing methods for hypernym detection rely on word distribution. |
| Approach: | They propose a model to learn box embeddings for hypernym discovery by using a dataset . they compare the performance of their model on medical and music domains . |
| Outcome: | The proposed model outperforms existing methods on most evaluation metrics on medical and music domains. |
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| Challenge: | Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model. |
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Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
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| Challenge: | supervised hypernymy detection suffers from overfitting hypernies in training data. |
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| Challenge: | Existing unsupervised methods for learning hypernyms from unlabeled text are not scaled to large vocabularies or yield unacceptably poor accuracy. |
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| Challenge: | Existing work on lexical relations based on distributed representations has differed widely. |
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| Challenge: | Existing methods for extracting hypernyms focus on the acquisition of binary hypernies . |
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Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings (2022.acl-long)
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Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum
| Challenge: | Word2Box provides a set-theoretic training objective for learning word representations . word representation is not natural, all senses and contexts, levels of abstraction, variants and modifications which the word may represent are forced to be captured by mat t is nunc. |
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The Effectiveness of Simple Hybrid Systems for Hypernym Discovery (P19-1)
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| Challenge: | Recent work utilizing a mix of pattern-based and distributional approaches have yielded state-of-the-art results on two domain-specific English hypernym discovery tasks. |
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HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning (2021.findings-emnlp)
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| Challenge: | Existing taxonomies have limited coverage due to expensive manual curation process. |
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More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods (2021.findings-acl)
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| Challenge: | Using unsupervised methods of hypernymy prediction, we show that the predictions of three methods overlap and are highly correlated with frequency-based predictions. |
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