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Hypernym Discovery
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Given a corpus and a target term (hyponym), the task of hypernym discovery consists of extracting a set of its most appropriate hypernyms from the corpus. For example, for the input word “dog”, some valid hypernyms would be “canine”, “mammal” or “animal”.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
Probing Pretrained Language Models with Hierarchy Properties
Modelling Commonsense Properties using Pre-Trained Bi-Encoders
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings
Document Structure aware Relational Graph Convolutional Networks for Ontology Population
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
Document Structure Measure for Hypernym discovery
SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.