- Description:
 
GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications.
Additional Documentation: Explore on Papers With Code
Homepage: https://github.com/google-research-datasets/gap-coreference
Source code:
tfds.text.GapVersions:
0.1.0: Initial release.0.1.1(default): Fixes parsing of boolean fieldA-corefandB-coref.
Download size:
2.29 MiBDataset size:
2.96 MiBAuto-cached (documentation): Yes
Splits:
| Split | Examples | 
|---|---|
'test' | 
2,000 | 
'train' | 
2,000 | 
'validation' | 
454 | 
- Feature structure:
 
FeaturesDict({
    'A': Text(shape=(), dtype=string),
    'A-coref': bool,
    'A-offset': int32,
    'B': Text(shape=(), dtype=string),
    'B-coref': bool,
    'B-offset': int32,
    'ID': Text(shape=(), dtype=string),
    'Pronoun': Text(shape=(), dtype=string),
    'Pronoun-offset': int32,
    'Text': Text(shape=(), dtype=string),
    'URL': Text(shape=(), dtype=string),
})
- Feature documentation:
 
| Feature | Class | Shape | Dtype | Description | 
|---|---|---|---|---|
| FeaturesDict | ||||
| A | Text | string | ||
| A-coref | Tensor | bool | ||
| A-offset | Tensor | int32 | ||
| B | Text | string | ||
| B-coref | Tensor | bool | ||
| B-offset | Tensor | int32 | ||
| ID | Text | string | ||
| Pronoun | Text | string | ||
| Pronoun-offset | Tensor | int32 | ||
| Text | Text | string | ||
| URL | Text | string | 
Supervised keys (See
as_superviseddoc):NoneFigure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
 
@article{DBLP:journals/corr/abs-1810-05201,
  author    = {Kellie Webster and
               Marta Recasens and
               Vera Axelrod and
               Jason Baldridge},
  title     = {Mind the {GAP:} {A} Balanced Corpus of Gendered Ambiguous Pronouns},
  journal   = {CoRR},
  volume    = {abs/1810.05201},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.05201},
  archivePrefix = {arXiv},
  eprint    = {1810.05201},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1810-05201},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}