Named Entity Disambiguation using Deep Learning on Graphs
Paper • 1810.09164 • Published
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Dataset Card for "Widdd"
Dataset Description
WiDDD stands for WIkiData Disambig with Descriptions. The former dataset comes from Cetoli & al paper, and is aimed at solving Named Entity Disambiguation. This datasets tries to extract relevant information from entities descriptions only, instead of working with graphs. In order to do so, we mapped every Wikidata id (correct id and wrong id) in the original paper with its WikiData description. If not found, row is discarded for the 1.+ versions.
Supported Tasks and Leaderboards
Languages
english
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
plain_text
- Size of downloaded dataset files: 46.64 MB
An example of 'train' looks as follows.
{'example_id': 11,
'string': 'pausanias',
'text': ' mention the spear, which he would indeed have touched with excitement. But it was being shown in the time of Pausanias in the second century AD. Achilles and ',
'correct_id': 'Q192931',
'wrong_id': 'Q941521',
'correct_description': 'ancient Greek geographer, travel writer and mythographer',
'wrong_description': 'Wikimedia disambiguation page'}
Data Fields
The data fields are the same among all splits.
plain_text
example_id: anint32feature,string: astringfeature,text: astringfeature,correct_id: astringfeature,wrong_id: astringfeature,correct_description: astringfeature,wrong_description: astringfeature,
Data Splits
| name | train | validation | test |
|---|---|---|---|
| plain_text | 96523 | 9609 | 9584 |
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