Papers with Code · CC BY-SA 4.0

Hypernym Discovery

Taxonomy Learning › Hypernym Discovery 23 평가 기록 3 datasets

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

300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes

2018-06-01 이 페이지의 평가 3건

MAP: 8.95MRR: 19.44P@5: 8.63

CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery

2018-06-01 이 페이지의 평가 3건

MAP: 19.78MRR: 36.10P@5: 19.03

Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

1612.04460 2016-12-14 이 페이지의 평가 3건

MAP: 1.36MRR: 3.18P@5: 1.30

SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings

1805.10465 2018-05-26 이 페이지의 평가 3건

MAP: 5.77MRR: 10.56P@5: 5.96

Supervised Distributional Hypernym Discovery via Domain Adaptation

2016-11-01 이 페이지의 평가 3건

MAP: 10.60MRR: 23.83P@5: 9.91

ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora

2018-06-01 이 페이지의 평가 2건

MAP: 2.63MRR: 7.46P@5: 2.64

Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies

2018-06-01 이 페이지의 평가 1건

MAP: 2.68MRR: 6.01P@5: 2.69

EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery

2018-06-01 이 페이지의 평가 1건

MAP: 13.77MRR: 40.76P@5: 12.76

NLP\_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach

2018-06-01 이 페이지의 평가 1건

MAP: 9.37MRR: 17.29P@5: 9.19