| Challenge: | Existing methods for extracting hypernyms focus on the acquisition of binary hypernies . |
| Approach: | They propose a distributionally-induced semantic class for extracting hypernyms . they also use distributional semantics to induce sense-aware semantic classes . |
| Outcome: | The proposed method improves the quality of the hypernymy extraction in terms of precision and recall. |
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| Challenge: | supervised hypernymy detection suffers from overfitting hypernies in training data. |
| Approach: | They propose a method that can alleviate the problem of overfitting hypernyms in training data by using distributional representations for unknown word pairs. |
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Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (D18-1)
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| Challenge: | Lexicon relation extraction given distributional representation of words is an important topic in NLP. |
| Approach: | They propose to use a word relation autoencoder to extract hypernyms from vocabularies . they propose to analyze the pollution and construct an indicator to measure it . |
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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. |
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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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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. |
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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. |
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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. |
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To Word Senses and Beyond: Inducing Concepts with Contextualized Language Models (2024.emnlp-main)
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| Challenge: | Word Sense Disambiguiation and Word sense Induction are considered independent problems, but they are often neglected in practice. |
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Semantic Specialization of Distributional Word Vectors (D19-2)
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| Challenge: | Distributional word vectors conflate various paradigmatic and syntagmatic lexico-semantic relations. |
| Approach: | This tutorial provides an overview of specialization methods for distributional word vectors . a common solution is to include external lexico-semantic knowledge in a reshaped vector space . |
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Semantic Frame Induction from a Real-World Corpus (2025.acl-srw)
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| Challenge: | Existing studies on semantic frame induction have demonstrated that pre-trained language models (PLMs) have led to more accurate results. |
| Approach: | They conduct semantic frame induction using the Colossal Clean Crawled Corpus and assess the applicability of existing frame inducing methods to real-world data. |
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