Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
ArXiv:
metadata
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: CrossNER is a cross-domain dataset for named entity recognition
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
tags:
- cross domain
- ai
- news
- music
- literature
- politics
- science
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
- config_name: ai
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academicjournal
'2': I-academicjournal
'3': B-album
'4': I-album
'5': B-algorithm
'6': I-algorithm
'7': B-astronomicalobject
'8': I-astronomicalobject
'9': B-award
'10': I-award
'11': B-band
'12': I-band
'13': B-book
'14': I-book
'15': B-chemicalcompound
'16': I-chemicalcompound
'17': B-chemicalelement
'18': I-chemicalelement
'19': B-conference
'20': I-conference
'21': B-country
'22': I-country
'23': B-discipline
'24': I-discipline
'25': B-election
'26': I-election
'27': B-enzyme
'28': I-enzyme
'29': B-event
'30': I-event
'31': B-field
'32': I-field
'33': B-literarygenre
'34': I-literarygenre
'35': B-location
'36': I-location
'37': B-magazine
'38': I-magazine
'39': B-metrics
'40': I-metrics
'41': B-misc
'42': I-misc
'43': B-musicalartist
'44': I-musicalartist
'45': B-musicalinstrument
'46': I-musicalinstrument
'47': B-musicgenre
'48': I-musicgenre
'49': B-organisation
'50': I-organisation
'51': B-person
'52': I-person
'53': B-poem
'54': I-poem
'55': B-politicalparty
'56': I-politicalparty
'57': B-politician
'58': I-politician
'59': B-product
'60': I-product
'61': B-programlang
'62': I-programlang
'63': B-protein
'64': I-protein
'65': B-researcher
'66': I-researcher
'67': B-scientist
'68': I-scientist
'69': B-song
'70': I-song
'71': B-task
'72': I-task
'73': B-theory
'74': I-theory
'75': B-university
'76': I-university
'77': B-writer
'78': I-writer
splits:
- name: train
num_bytes: 65080
num_examples: 100
- name: validation
num_bytes: 189453
num_examples: 350
- name: test
num_bytes: 225691
num_examples: 431
download_size: 289173
dataset_size: 480224
- config_name: literature
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academicjournal
'2': I-academicjournal
'3': B-album
'4': I-album
'5': B-algorithm
'6': I-algorithm
'7': B-astronomicalobject
'8': I-astronomicalobject
'9': B-award
'10': I-award
'11': B-band
'12': I-band
'13': B-book
'14': I-book
'15': B-chemicalcompound
'16': I-chemicalcompound
'17': B-chemicalelement
'18': I-chemicalelement
'19': B-conference
'20': I-conference
'21': B-country
'22': I-country
'23': B-discipline
'24': I-discipline
'25': B-election
'26': I-election
'27': B-enzyme
'28': I-enzyme
'29': B-event
'30': I-event
'31': B-field
'32': I-field
'33': B-literarygenre
'34': I-literarygenre
'35': B-location
'36': I-location
'37': B-magazine
'38': I-magazine
'39': B-metrics
'40': I-metrics
'41': B-misc
'42': I-misc
'43': B-musicalartist
'44': I-musicalartist
'45': B-musicalinstrument
'46': I-musicalinstrument
'47': B-musicgenre
'48': I-musicgenre
'49': B-organisation
'50': I-organisation
'51': B-person
'52': I-person
'53': B-poem
'54': I-poem
'55': B-politicalparty
'56': I-politicalparty
'57': B-politician
'58': I-politician
'59': B-product
'60': I-product
'61': B-programlang
'62': I-programlang
'63': B-protein
'64': I-protein
'65': B-researcher
'66': I-researcher
'67': B-scientist
'68': I-scientist
'69': B-song
'70': I-song
'71': B-task
'72': I-task
'73': B-theory
'74': I-theory
'75': B-university
'76': I-university
'77': B-writer
'78': I-writer
splits:
- name: train
num_bytes: 63181
num_examples: 100
- name: validation
num_bytes: 244076
num_examples: 400
- name: test
num_bytes: 270092
num_examples: 416
download_size: 334380
dataset_size: 577349
- config_name: music
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academicjournal
'2': I-academicjournal
'3': B-album
'4': I-album
'5': B-algorithm
'6': I-algorithm
'7': B-astronomicalobject
'8': I-astronomicalobject
'9': B-award
'10': I-award
'11': B-band
'12': I-band
'13': B-book
'14': I-book
'15': B-chemicalcompound
'16': I-chemicalcompound
'17': B-chemicalelement
'18': I-chemicalelement
'19': B-conference
'20': I-conference
'21': B-country
'22': I-country
'23': B-discipline
'24': I-discipline
'25': B-election
'26': I-election
'27': B-enzyme
'28': I-enzyme
'29': B-event
'30': I-event
'31': B-field
'32': I-field
'33': B-literarygenre
'34': I-literarygenre
'35': B-location
'36': I-location
'37': B-magazine
'38': I-magazine
'39': B-metrics
'40': I-metrics
'41': B-misc
'42': I-misc
'43': B-musicalartist
'44': I-musicalartist
'45': B-musicalinstrument
'46': I-musicalinstrument
'47': B-musicgenre
'48': I-musicgenre
'49': B-organisation
'50': I-organisation
'51': B-person
'52': I-person
'53': B-poem
'54': I-poem
'55': B-politicalparty
'56': I-politicalparty
'57': B-politician
'58': I-politician
'59': B-product
'60': I-product
'61': B-programlang
'62': I-programlang
'63': B-protein
'64': I-protein
'65': B-researcher
'66': I-researcher
'67': B-scientist
'68': I-scientist
'69': B-song
'70': I-song
'71': B-task
'72': I-task
'73': B-theory
'74': I-theory
'75': B-university
'76': I-university
'77': B-writer
'78': I-writer
splits:
- name: train
num_bytes: 65077
num_examples: 100
- name: validation
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num_examples: 380
- name: test
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num_examples: 465
download_size: 414065
dataset_size: 651974
- config_name: conll2003
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academicjournal
'2': I-academicjournal
'3': B-album
'4': I-album
'5': B-algorithm
'6': I-algorithm
'7': B-astronomicalobject
'8': I-astronomicalobject
'9': B-award
'10': I-award
'11': B-band
'12': I-band
'13': B-book
'14': I-book
'15': B-chemicalcompound
'16': I-chemicalcompound
'17': B-chemicalelement
'18': I-chemicalelement
'19': B-conference
'20': I-conference
'21': B-country
'22': I-country
'23': B-discipline
'24': I-discipline
'25': B-election
'26': I-election
'27': B-enzyme
'28': I-enzyme
'29': B-event
'30': I-event
'31': B-field
'32': I-field
'33': B-literarygenre
'34': I-literarygenre
'35': B-location
'36': I-location
'37': B-magazine
'38': I-magazine
'39': B-metrics
'40': I-metrics
'41': B-misc
'42': I-misc
'43': B-musicalartist
'44': I-musicalartist
'45': B-musicalinstrument
'46': I-musicalinstrument
'47': B-musicgenre
'48': I-musicgenre
'49': B-organisation
'50': I-organisation
'51': B-person
'52': I-person
'53': B-poem
'54': I-poem
'55': B-politicalparty
'56': I-politicalparty
'57': B-politician
'58': I-politician
'59': B-product
'60': I-product
'61': B-programlang
'62': I-programlang
'63': B-protein
'64': I-protein
'65': B-researcher
'66': I-researcher
'67': B-scientist
'68': I-scientist
'69': B-song
'70': I-song
'71': B-task
'72': I-task
'73': B-theory
'74': I-theory
'75': B-university
'76': I-university
'77': B-writer
'78': I-writer
splits:
- name: train
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num_examples: 14041
- name: validation
num_bytes: 891431
num_examples: 3250
- name: test
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num_examples: 3453
download_size: 2694794
dataset_size: 5263982
- config_name: politics
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academicjournal
'2': I-academicjournal
'3': B-album
'4': I-album
'5': B-algorithm
'6': I-algorithm
'7': B-astronomicalobject
'8': I-astronomicalobject
'9': B-award
'10': I-award
'11': B-band
'12': I-band
'13': B-book
'14': I-book
'15': B-chemicalcompound
'16': I-chemicalcompound
'17': B-chemicalelement
'18': I-chemicalelement
'19': B-conference
'20': I-conference
'21': B-country
'22': I-country
'23': B-discipline
'24': I-discipline
'25': B-election
'26': I-election
'27': B-enzyme
'28': I-enzyme
'29': B-event
'30': I-event
'31': B-field
'32': I-field
'33': B-literarygenre
'34': I-literarygenre
'35': B-location
'36': I-location
'37': B-magazine
'38': I-magazine
'39': B-metrics
'40': I-metrics
'41': B-misc
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'43': B-musicalartist
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'45': B-musicalinstrument
'46': I-musicalinstrument
'47': B-musicgenre
'48': I-musicgenre
'49': B-organisation
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'51': B-person
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'53': B-poem
'54': I-poem
'55': B-politicalparty
'56': I-politicalparty
'57': B-politician
'58': I-politician
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'60': I-product
'61': B-programlang
'62': I-programlang
'63': B-protein
'64': I-protein
'65': B-researcher
'66': I-researcher
'67': B-scientist
'68': I-scientist
'69': B-song
'70': I-song
'71': B-task
'72': I-task
'73': B-theory
'74': I-theory
'75': B-university
'76': I-university
'77': B-writer
'78': I-writer
splits:
- name: train
num_bytes: 143507
num_examples: 200
- name: validation
num_bytes: 422760
num_examples: 541
- name: test
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num_examples: 651
download_size: 724168
dataset_size: 1038957
- config_name: science
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academicjournal
'2': I-academicjournal
'3': B-album
'4': I-album
'5': B-algorithm
'6': I-algorithm
'7': B-astronomicalobject
'8': I-astronomicalobject
'9': B-award
'10': I-award
'11': B-band
'12': I-band
'13': B-book
'14': I-book
'15': B-chemicalcompound
'16': I-chemicalcompound
'17': B-chemicalelement
'18': I-chemicalelement
'19': B-conference
'20': I-conference
'21': B-country
'22': I-country
'23': B-discipline
'24': I-discipline
'25': B-election
'26': I-election
'27': B-enzyme
'28': I-enzyme
'29': B-event
'30': I-event
'31': B-field
'32': I-field
'33': B-literarygenre
'34': I-literarygenre
'35': B-location
'36': I-location
'37': B-magazine
'38': I-magazine
'39': B-metrics
'40': I-metrics
'41': B-misc
'42': I-misc
'43': B-musicalartist
'44': I-musicalartist
'45': B-musicalinstrument
'46': I-musicalinstrument
'47': B-musicgenre
'48': I-musicgenre
'49': B-organisation
'50': I-organisation
'51': B-person
'52': I-person
'53': B-poem
'54': I-poem
'55': B-politicalparty
'56': I-politicalparty
'57': B-politician
'58': I-politician
'59': B-product
'60': I-product
'61': B-programlang
'62': I-programlang
'63': B-protein
'64': I-protein
'65': B-researcher
'66': I-researcher
'67': B-scientist
'68': I-scientist
'69': B-song
'70': I-song
'71': B-task
'72': I-task
'73': B-theory
'74': I-theory
'75': B-university
'76': I-university
'77': B-writer
'78': I-writer
splits:
- name: train
num_bytes: 121928
num_examples: 200
- name: validation
num_bytes: 276118
num_examples: 450
- name: test
num_bytes: 334181
num_examples: 543
download_size: 485191
dataset_size: 732227
Dataset Card for CrossRE
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: CrossNER
- Paper: CrossNER: Evaluating Cross-Domain Named Entity Recognition
Dataset Summary
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five domains.
For details, see the paper: CrossNER: Evaluating Cross-Domain Named Entity Recognition
Supported Tasks and Leaderboards
Languages
The language data in CrossNER is in English (BCP-47 en)
Dataset Structure
Data Instances
conll2003
- Size of downloaded dataset files: 2.69 MB
- Size of the generated dataset: 5.26 MB
An example of 'train' looks as follows:
{
"id": "0",
"tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner_tags": [49, 0, 41, 0, 0, 0, 41, 0, 0]
}
politics
- Size of downloaded dataset files: 0.72 MB
- Size of the generated dataset: 1.04 MB
An example of 'train' looks as follows:
{
"id": "0",
"tokens": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 55, 56, 0, 0, 0, 0, 0, 55, 56, 56, 56, 56, 56, 0, 55, 56, 56, 56, 56, 0]
}
science
- Size of downloaded dataset files: 0.49 MB
- Size of the generated dataset: 0.73 MB
An example of 'train' looks as follows:
{
"id": "0",
"tokens": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner_tags": [0, 0, 0, 0, 15, 16, 0, 15, 16, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
music
- Size of downloaded dataset files: 0.41 MB
- Size of the generated dataset: 0.65 MB
An example of 'train' looks as follows:
{
"id": "0",
"tokens": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner_tags": [0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 29, 30, 30, 30, 30, 0]
}
literature
- Size of downloaded dataset files: 0.33 MB
- Size of the generated dataset: 0.58 MB
An example of 'train' looks as follows:
{
"id": "0",
"tokens": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 51, 52, 52, 0, 0, 21, 22, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 21, 0, 21, 0, 0, 41, 0, 0, 0, 0, 0, 0, 51, 52, 0, 0, 41, 0, 0, 0, 0, 0, 51, 0, 0]
}
ai
- Size of downloaded dataset files: 0.29 MB
- Size of the generated dataset: 0.48 MB
An example of 'train' looks as follows:
{
"id": "0",
"tokens": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner_tags": [0, 0, 0, 59, 60, 60, 0, 0, 0, 0, 31, 32, 0, 71, 72, 0, 71, 72, 0, 0, 0, 71, 72, 72, 0, 0, 31, 32, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
Data Fields
The data fields are the same among all splits.
id: the instance id of this sentence, astringfeature.tokens: the list of tokens of this sentence, alistofstringfeatures.ner_tags: the list of entity tags, alistof classification labels.
{"O": 0, "B-academicjournal": 1, "I-academicjournal": 2, "B-album": 3, "I-album": 4, "B-algorithm": 5, "I-algorithm": 6, "B-astronomicalobject": 7, "I-astronomicalobject": 8, "B-award": 9, "I-award": 10, "B-band": 11, "I-band": 12, "B-book": 13, "I-book": 14, "B-chemicalcompound": 15, "I-chemicalcompound": 16, "B-chemicalelement": 17, "I-chemicalelement": 18, "B-conference": 19, "I-conference": 20, "B-country": 21, "I-country": 22, "B-discipline": 23, "I-discipline": 24, "B-election": 25, "I-election": 26, "B-enzyme": 27, "I-enzyme": 28, "B-event": 29, "I-event": 30, "B-field": 31, "I-field": 32, "B-literarygenre": 33, "I-literarygenre": 34, "B-location": 35, "I-location": 36, "B-magazine": 37, "I-magazine": 38, "B-metrics": 39, "I-metrics": 40, "B-misc": 41, "I-misc": 42, "B-musicalartist": 43, "I-musicalartist": 44, "B-musicalinstrument": 45, "I-musicalinstrument": 46, "B-musicgenre": 47, "I-musicgenre": 48, "B-organisation": 49, "I-organisation": 50, "B-person": 51, "I-person": 52, "B-poem": 53, "I-poem": 54, "B-politicalparty": 55, "I-politicalparty": 56, "B-politician": 57, "I-politician": 58, "B-product": 59, "I-product": 60, "B-programlang": 61, "I-programlang": 62, "B-protein": 63, "I-protein": 64, "B-researcher": 65, "I-researcher": 66, "B-scientist": 67, "I-scientist": 68, "B-song": 69, "I-song": 70, "B-task": 71, "I-task": 72, "B-theory": 73, "I-theory": 74, "B-university": 75, "I-university": 76, "B-writer": 77, "I-writer": 78}
Data Splits
| Train | Dev | Test | |
|---|---|---|---|
| conll2003 | 14,987 | 3,466 | 3,684 |
| politics | 200 | 541 | 651 |
| science | 200 | 450 | 543 |
| music | 100 | 380 | 456 |
| literature | 100 | 400 | 416 |
| ai | 100 | 350 | 431 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{liu2020crossner,
title={CrossNER: Evaluating Cross-Domain Named Entity Recognition},
author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
year={2020},
eprint={2012.04373},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Contributions
Thanks to @phucdev for adding this dataset.