Dataset Card for "conllpp"

Dataset Summary

CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set have been manually corrected. The training set and development set from CoNLL2003 is included for completeness. One correction on the test set for example, is:

{
    "tokens": ["SOCCER", "-", "JAPAN", "GET", "LUCKY", "WIN", ",", "CHINA", "IN", "SURPRISE", "DEFEAT", "."],
    "original_ner_tags_in_conll2003": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-PER", "O", "O", "O", "O"],
    "corrected_ner_tags_in_conllpp": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-LOC", "O", "O", "O", "O"],
}

Supported Tasks and Leaderboards

[More Information Needed]

Languages

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Dataset Structure

Data Instances

conllpp

• Size of downloaded dataset files: 4.85 MB
• Size of the generated dataset: 10.26 MB
• Total amount of disk used: 15.11 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "id": "0",
    "document_id": 1,
    "sentence_id": 3,
    "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
    "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
    "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
}

Data Fields

The data fields are the same among all splits.

conllpp

• id : a string feature.
• document_id : an int32 feature tracking which document the sample is from.
• sentence_id : an int32 feature tracking which sentence in this document the sample is from.
• tokens : a list of string features.
• pos_tags : a list of classification labels, with possible values including " (0), '' (1), # (2), $ (3), ( (4).
• chunk_tags : a list of classification labels, with possible values including O (0), B-ADJP (1), I-ADJP (2), B-ADVP (3), I-ADVP (4).
• ner_tags : a list of classification labels, with possible values including O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4).

Data Splits

name	train	validation	test
conll2003	14041	3250	3453

Dataset Creation

Curation Rationale

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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

@inproceedings{wang2019crossweigh,
  title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
  author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={5157--5166},
  year={2019}
}

Contributions

Thanks to @ZihanWangKi for adding this dataset.