child
stringlengths
1
71
parent
stringlengths
1
71
label
int64
0
1
boarhound
hound
1
boarhound
beagle
0
boarhound
afghan hound
0
boarhound
staghound
0
boarhound
wolfhound
0
boarhound
ibizan hound
0
boarhound
plott hound
0
boarhound
foxhound
0
boarhound
norwegian elkhound
0
boarhound
weimaraner
0
boarhound
redbone
0
chin
feature
1
chin
jaw
0
chin
jowl
0
chin
cheek
0
chin
brow
0
chin
temple
0
chin
hydremia
0
chin
deserter
0
chin
suppressor
0
chin
tilefish
0
chin
damp
0
fertilization
creation
1
fertilization
recess
0
fertilization
walk-through
0
fertilization
ileocolic vein
0
fertilization
kopek
0
fertilization
poon
0
fertilization
elasticity
0
fertilization
summer school
0
fertilization
water vapor
0
fertilization
billiard ball
0
fertilization
fugue
0
hulk
ship
1
hulk
treasure ship
0
hulk
whaler
0
hulk
abandoned ship
0
hulk
gas-turbine ship
0
hulk
school ship
0
hulk
hospital ship
0
hulk
icebreaker
0
hulk
pirate
0
hulk
troopship
0
hulk
passenger ship
0
change of location
movement
1
change of location
periodic motion
0
change of location
crustal movement
0
change of location
turning
0
change of location
recoil
0
change of location
brownian movement
0
change of location
throw
0
change of location
wave
0
change of location
passing
0
change of location
twist
0
change of location
bending
0
pheasant
wildfowl
1
pheasant
quail
0
pheasant
grouse
0
pheasant
partridge
0
pheasant
radar echo
0
pheasant
transfusion
0
pheasant
deep-freeze
0
pheasant
scotch kiss
0
pheasant
tollbooth
0
pheasant
bevatron
0
pheasant
genus tragopan
0
posterior synechia
synechia
1
posterior synechia
anterior synechia
0
posterior synechia
humanitarianism
0
posterior synechia
acerola
0
posterior synechia
sericea lespedeza
0
posterior synechia
religious orientation
0
posterior synechia
point
0
posterior synechia
pulp
0
posterior synechia
airfoil
0
posterior synechia
altruism
0
posterior synechia
alliaria
0
paradoxical sleep
sleep
1
paradoxical sleep
sleeping
0
paradoxical sleep
shuteye
0
paradoxical sleep
orthodox sleep
0
paradoxical sleep
acanthuridae
0
paradoxical sleep
shrike
0
paradoxical sleep
urticaceae
0
paradoxical sleep
fabrication
0
paradoxical sleep
transition
0
paradoxical sleep
pretension
0
paradoxical sleep
stretcher
0
referred pain
pain
1
referred pain
sting
0
referred pain
causalgia
0
referred pain
glossalgia
0
referred pain
pang
0
referred pain
pleurodynia
0
referred pain
stitch
0
referred pain
distress
0
referred pain
throb
0
referred pain
tenderness
0
referred pain
hemorrhoid
0
diagnostician
specialist
1

Dataset Card for WordNetNoun

This dataset is a collection of Multi-hop Inference and Mixed-hop Prediction datasets created from WordNet's subsumption (hypernym) hierarchy of noun entities for training and evaluating hierarchy embedding models.

  • Multi-hop Inference: This task aims to evaluate the model’s ability in deducing indirect, multi-hop subsumptions from direct, one-hop subsumptions, so as to simulate transitive inference.
  • Mixed-hop Prediction: This task aims to evaluate the model’s capability in determining the existence of subsumption relationships between arbitrary entity pairs, where the entities are not necessarily seen during training. The transfer setting of this task involves training models on asserted training edges of one hierarchy testing on arbitrary entity pairs of another.

See our published paper for more detail.

Links

The information of original entity IDs is not available in the Huggingface release; To map entities back to their original hierarchies, refer to this Zenodo release.

Dataset Structure

Each subset in this dataset follows the naming convention TaskType-NegativeType-SampleStructure:

  • TaskType: Either MultiHop or MixedHop, indicating the type of hierarchy evaluation task.

  • NegativeType: Either RandomNegatives or HardNegatives, specifying the strategy used for negative sampling.

  • SampleStructure: Either Triplets or Pairs, indicating the format of the samples.

    • In Triplets, each sample is structured as (child, parent, negative).
    • In Pairs, each sample is a labelled pair (child, parent, label), where label=1 denotes a positive subsumption and label=0 denotes a negative subsumption.

For example, to load a subset for the Mixed-hop Prediction task with random negatives and samples presented as triplets, we can use the following command:

from datasets import load_dataset
dataset = load_dataset("Hierarchy-Transformers/WordNetNoun", "MixedHop-RandomNegatives-Triplets")

Dataset Usage

  • For evaluation, the Pairs sample structure should be adopted, as it allows for the computation of Precision, Recall, and F1 scores.

  • For training, the choice between Pairs, Triplets, or more complex sample structures depends on the model's design and specific requirements.

Citation

The relevant paper has been accepted at NeurIPS 2024 (to appear).

@article{he2024language,
  title={Language models as hierarchy encoders},
  author={He, Yuan and Yuan, Zhangdie and Chen, Jiaoyan and Horrocks, Ian},
  journal={arXiv preprint arXiv:2401.11374},
  year={2024}
}

Contact

Yuan He (yuan.he(at)cs.ox.ac.uk)

Downloads last month
46
Edit dataset card