https://github.com/amitgajbhiye/biencoder_concept_property
공식 코드Modelling Commonsense Properties using Pre-Trained Bi-Encoders
Papers with Code 아카이브
Hypernym Discovery
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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”.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dalab/hyperbolic_cones
공식 코드Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
https://github.com/vered1986/UnsupervisedHypernymy
공식 코드Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
https://github.com/vityavitalich/taxollama
공식 코드TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
https://github.com/zjukg/ontotune
공식 코드OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
https://github.com/dinobby/hypemo
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
https://github.com/gbcolborne/hypernym_discovery
https://github.com/iesl/geometric_graph_embedding
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.