| 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. |
| Approach: | They evaluate the contribution of pattern-based and distributional approaches to hybrid modeling by evaluating baseline models from each paradigm. |
| Outcome: | The proposed approach outperforms all non-hybrid approaches on two domain-specific English hypernym discovery tasks and outperformed other approaches. |
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Hypernym Discovery via a Recurrent Mapping Model (2021.findings-acl)
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| Challenge: | Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model. |
| Approach: | They propose a parallel style model that maps query words to their hypernyms . they use a lexical-semantic relation to name a specific instance or subtype hyponym . |
| Outcome: | Empirical results on SemEval-2018 Task 9 confirm the effectiveness of the proposed model. |
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. |
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora (P18-2)
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| Challenge: | a well-known problem of Hearst-like patterns is their extreme sparsity. |
| Approach: | They propose to use pattern-based and distributional methods to perform unsupervised hypernym detection. |
| Outcome: | The proposed method outperforms distributional methods on hypernymy tasks. |
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models (2020.emnlp-main)
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| Challenge: | a taxonomy is a semantic hierarchy of words or concepts organized w.r.t. their hypernymy relationships. |
| Approach: | They propose a framework for hypernymy detection using large textual corpora . they quantify the non-negligible existence of specific sparsity cases . |
| Outcome: | The proposed framework quantifies the non-negligible existence of specific sparsity cases on several benchmark datasets. |
Hypernymy Detection for Low-Resource Languages via Meta Learning (2020.acl-main)
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| Challenge: | Existing studies focus on monolingual hypernymy detection on high-resource languages, but few investigate low-resourced scenarios. |
| Approach: | They propose to combine high-resource languages to solve low-resourced hypernymy detection problem . they extensively compare three joint training paradigms and propose meta learning . |
| Outcome: | The proposed method significantly improves performance of extremely low-resource languages by preventing over-fitting on small datasets. |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (N18-1)
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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. |
| Approach: | They propose an unsupervised method of hypernym discovery using word contexts . they use word2vec to embed word context distributions without supervision . |
| Outcome: | The proposed method provides double the precision and highest average performance on 11 datasets. |
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. |
| Approach: | They compare unsupervised methods of hypernymy prediction to supervised methods . they show that the methods overlap and are highly correlated with frequency-based predictions . |
| Outcome: | The proposed methods overlap and are highly correlated with frequency-based predictions across English and German datasets. |
Leveraging WordNet Paths for Neural Hypernym Prediction (2020.coling-main)
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| Challenge: | Existing work on lexical relations based on distributed representations has differed widely. |
| Approach: | They propose a model that generates taxonomy paths for hypernym prediction using WordNet sequences. |
| Outcome: | The hypo2path model outperforms the best model by 4.11 points in hit-at-one (H@1) The proposed model outpersforms previous models by a factor of 0.9. |
Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
| Approach: | They propose two techniques which generate new training examples from existing ones . they combine linguistic principles of hypernym transitivity and intersective modifier-noun composition . |
| Outcome: | The proposed techniques generate new training examples from existing datasets. |
A Multi-dimensional Evaluation of Tokenizer-free Multilingual Pretrained Models (2023.findings-eacl)
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| Challenge: | Recent work on tokenizer-free models shows promising results in cross-lingual transfer . previous work focused on reporting accuracy on a limited set of tasks and data settings . |
| Approach: | They compare tokenizer-free and subword-based models using various dimensions . they find subword models are still the most practical choice in many settings . |
| Outcome: | The proposed model improves cross-lingual transfer and reduces engineering overhead. |