Datasets:

DFKI-SLT
/

cross_ner

	---
	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
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	'6': I-algorithm
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	'21': B-country
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	'23': B-discipline
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	'43': B-musicalartist
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	'45': B-musicalinstrument
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	'47': B-musicgenre
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	'49': B-organisation
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	'51': B-person
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	'53': B-poem
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	'61': B-programlang
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	'63': B-protein
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	'68': I-scientist
	'69': B-song
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	- name: validation
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	num_examples: 350
	- name: test
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	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:
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	num_examples: 416
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	dataset_size: 577349
	- config_name: music
	features:
	- name: id
	dtype: string
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
	names:
	'0': O
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	- name: validation
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	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:
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	- config_name: politics
	features:
	- name: id
	dtype: string
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
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	- config_name: science
	features:
	- name: id
	dtype: string
	- name: tokens
	sequence: string
	- name: ner_tags
	sequence:
	class_label:
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	- name: validation
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	- name: test
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	download_size: 485191
	dataset_size: 732227
	---
	# Dataset Card for CrossRE
	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description
	- Repository: [CrossNER](https://github.com/zliucr/CrossNER)
	- Paper: [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)

	### 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](https://arxiv.org/abs/2012.04373)

	### Supported Tasks and Leaderboards

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### 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:
	```json
	{
	"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:
	```json
	{
	"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:
	```json
	{
	"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:
	```json
	{
	"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:
	```json
	{
	"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:
	```json
	{
	"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, a `string` feature.
	- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
	- `ner_tags`: the list of entity tags, a `list` of classification labels.

	```json
	{"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

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the source language producers?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Annotations

	#### Annotation process

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the annotators?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Personal and Sensitive Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Discussion of Biases

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Other Known Limitations

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Additional Information

	### Dataset Curators

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Licensing Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### 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](https://github.com/phucdev) for adding this dataset.