Dataset Card for Polyglot-NER

Dataset Summary

Polyglot-NER A training dataset automatically generated from Wikipedia and Freebase the task of named entity recognition. The dataset contains the basic Wikipedia based training data for 40 languages we have (with coreference resolution) for the task of named entity recognition. The details of the procedure of generating them is outlined in Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data corresponding to a different language. For example, "es" includes only spanish examples.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

ar

• Size of downloaded dataset files: 1.11 GB
• Size of the generated dataset: 183.55 MB
• Total amount of disk used: 1.29 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "id": "2",
    "lang": "ar",
    "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "LOC", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "PER", "PER", "PER", "PER", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
    "words": "[\"وفي\", \"مرحلة\", \"موالية\", \"أنشأت\", \"قبيلة\", \"مكناسة\", \"الزناتية\", \"مكناسة\", \"تازة\", \",\", \"وأقام\", \"بها\", \"المرابطون\", \"قلعة\", \"..."
}

bg

• Size of downloaded dataset files: 1.11 GB
• Size of the generated dataset: 190.51 MB
• Total amount of disk used: 1.30 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "id": "1",
    "lang": "bg",
    "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
    "words": "[\"Дефиниция\", \"Наименованията\", \"\\\"\", \"книжовен\", \"\\\"/\\\"\", \"литературен\", \"\\\"\", \"език\", \"на\", \"български\", \"за\", \"тази\", \"кодифи..."
}

ca

• Size of downloaded dataset files: 1.11 GB
• Size of the generated dataset: 143.75 MB
• Total amount of disk used: 1.25 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "id": "2",
    "lang": "ca",
    "ner": "[\"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O...",
    "words": "[\"Com\", \"a\", \"compositor\", \"deixà\", \"un\", \"immens\", \"llegat\", \"que\", \"inclou\", \"8\", \"simfonies\", \"(\", \"1822\", \"),\", \"diverses\", ..."
}

combined

• Size of downloaded dataset files: 1.11 GB
• Size of the generated dataset: 6.29 GB
• Total amount of disk used: 7.39 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "id": "18",
    "lang": "es",
    "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
    "words": "[\"Los\", \"cambios\", \"en\", \"la\", \"energía\", \"libre\", \"de\", \"Gibbs\", \"\\\\\", \"Delta\", \"G\", \"nos\", \"dan\", \"una\", \"cuantificación\", \"de..."
}

cs

• Size of downloaded dataset files: 1.11 GB
• Size of the generated dataset: 156.79 MB
• Total amount of disk used: 1.26 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "id": "3",
    "lang": "cs",
    "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
    "words": "[\"Historie\", \"Symfonická\", \"forma\", \"se\", \"rozvinula\", \"se\", \"především\", \"v\", \"období\", \"klasicismu\", \"a\", \"romantismu\", \",\", \"..."
}

Data Fields

The data fields are the same among all splits.

ar

• id: a string feature.
• lang: a string feature.
• words: a list of string features.
• ner: a list of string features.

bg

• id: a string feature.
• lang: a string feature.
• words: a list of string features.
• ner: a list of string features.

ca

• id: a string feature.
• lang: a string feature.
• words: a list of string features.
• ner: a list of string features.

combined

• id: a string feature.
• lang: a string feature.
• words: a list of string features.
• ner: a list of string features.

cs

• id: a string feature.
• lang: a string feature.
• words: a list of string features.
• ner: a list of string features.

Data Splits

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{polyglotner,
         author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
         title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
         journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}},
         month     = {April},
         year      = {2015},
         publisher = {SIAM},
}

Contributions

Thanks to @joeddav for adding this dataset.