widdd / README.md
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Fix task tags
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metadata
annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
language:
  - en
license:
  - apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets: []
task_categories:
  - token-classification
task_ids:
  - word-sense-disambiguation
pretty_name: Wikidisamb Dataset with Descriptions
tags:
  - wikidata-disambiguation

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

More Information Needed

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: an int32 feature,
  • string: a string feature,
  • text: a string feature,
  • correct_id: a string feature,
  • wrong_id: a string feature,
  • correct_description: a string feature,
  • wrong_description: a string feature,

Data Splits

name train validation test
plain_text 96523 9609 9584

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

Contributions