| Challenge: | Existing methods to detect lexical relations have been used to identify them in both supervised and unsupervised ways. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relation with high accuracy and various network measures to perform better or at par with the state-of-the-art models. |
| Outcome: | The proposed model performs better or at par with the state-of-the-art models. |
Similar Papers
Using Distributional Thesaurus Embedding for Co-hyponymy Detection (2020.lrec-1)
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| Challenge: | Existing methods to detect lexical relations among distributionally similar words have been proposed to solve this problem. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relations by embedding them into the distributional thesaurus. |
| Outcome: | The proposed model outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-meronymy by huge margins. |
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. |
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. |
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)
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| Challenge: | Lexical relations are relations between terms in lexicons. |
| Approach: | They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks. |
Lexical Entailment with Hierarchy Representations by Deep Metric Learning (2022.findings-emnlp)
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| Challenge: | Existing lexical entailment studies cannot be applied to words that are not included in the training dataset. |
| Approach: | They propose a method that learns a mapping from word embeddings to hierarchical embedds to predict hypernymy relations among words. |
| Outcome: | The proposed method achieves state-of-the-art performance and robustness for unknown words. |
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 . |
| Outcome: | The proposed model outperforms the competitors on several hypernym-like lexicon datasets. |
Predicting Semantic Relations using Global Graph Properties (D18-1)
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| Challenge: | Semantic graphs encode the structural qualities of language as a representation of human knowledge. |
| Approach: | They propose a global-theoretic model that integrates global and local properties of semantic graphs to improve local prediction of relational relations between synsets. |
| Outcome: | The proposed model improves on the local task of predicting semantic relations between synsets, yielding state-of-the-art results on the WN18RR dataset. |
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations (N18-1)
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| Challenge: | Existing approaches to recognize lexical semantic relations between word pairs require that word pairs co-occur in a sentence. |
| Approach: | They propose to exploit lexico-syntactic paths between two target words to exploit the semantic relations between word pairs. |
| Outcome: | The proposed model can generalize the co-occurrences of word pairs and dependency paths and extract features capturing relational information from word pairs. |
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment (P19-1)
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| Challenge: | Lexical entailment (LE) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. |
| Approach: | They propose a generalized Lexical ENtailment model that captures a specialization function allowing for LE-tuning of the entire distributional vector space and not only the vectors of words seen in lexical constraints. |
| Outcome: | The proposed model improves on graded LE and shows 20% improvement over state-of-the-art LE detection. |
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks (2021.naacl-main)
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| Challenge: | Existing methods to explain neural network models are computationally inefficient for text inputs. |
| Approach: | They propose a method to implicitly detect word correlations by grouping correlated words from input text pairs together and measuring their contribution to corresponding NLP tasks. |
| Outcome: | The proposed method is evaluated with two different model architectures across four datasets. |