Hugging Face's logo

Datasets:
conllpp

Tasks:
Token Classification
Sub-tasks: named-entity-recognition
Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: extended|conll2003
Tags:
License:
Dataset card Viewer Files Community
4
conllpp / README.md
albertvillanova's picture
albertvillanova HF staff
Convert dataset sizes from base 2 to base 10 in the dataset card (#2)
3e60128
preview code
|
raw history blame contribute delete
No virus
7.7 kB
metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|conll2003
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: conll
pretty_name: CoNLL++
train-eval-index:
  - config: conllpp
    task: token-classification
    task_id: entity_extraction
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      tokens: tokens
      ner_tags: tags
    metrics:
      - type: seqeval
        name: seqeval
dataset_info:
  features:
    - name: id
      dtype: string
    - name: tokens
      sequence: string
    - name: pos_tags
      sequence:
        class_label:
          names:
            '0': '"'
            '1': ''''''
            '2': '#'
            '3': $
            '4': (
            '5': )
            '6': ','
            '7': .
            '8': ':'
            '9': '``'
            '10': CC
            '11': CD
            '12': DT
            '13': EX
            '14': FW
            '15': IN
            '16': JJ
            '17': JJR
            '18': JJS
            '19': LS
            '20': MD
            '21': NN
            '22': NNP
            '23': NNPS
            '24': NNS
            '25': NN|SYM
            '26': PDT
            '27': POS
            '28': PRP
            '29': PRP$
            '30': RB
            '31': RBR
            '32': RBS
            '33': RP
            '34': SYM
            '35': TO
            '36': UH
            '37': VB
            '38': VBD
            '39': VBG
            '40': VBN
            '41': VBP
            '42': VBZ
            '43': WDT
            '44': WP
            '45': WP$
            '46': WRB
    - name: chunk_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-ADJP
            '2': I-ADJP
            '3': B-ADVP
            '4': I-ADVP
            '5': B-CONJP
            '6': I-CONJP
            '7': B-INTJ
            '8': I-INTJ
            '9': B-LST
            '10': I-LST
            '11': B-NP
            '12': I-NP
            '13': B-PP
            '14': I-PP
            '15': B-PRT
            '16': I-PRT
            '17': B-SBAR
            '18': I-SBAR
            '19': B-UCP
            '20': I-UCP
            '21': B-VP
            '22': I-VP
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-ORG
            '4': I-ORG
            '5': B-LOC
            '6': I-LOC
            '7': B-MISC
            '8': I-MISC
  config_name: conllpp
  splits:
    - name: train
      num_bytes: 6931393
      num_examples: 14041
    - name: validation
      num_bytes: 1739247
      num_examples: 3250
    - name: test
      num_bytes: 1582078
      num_examples: 3453
  download_size: 4859600
  dataset_size: 10252718

Dataset Card for "conllpp"

Table of Contents

Dataset Description

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

[More Information Needed]

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:

{
    "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],
    "id": "0",
    "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],
    "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],
    "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", "."]
}

Data Fields

The data fields are the same among all splits.

conllpp

  • id: a string feature.
  • 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

[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 

@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.