# 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:** [CrossRE](https://github.com/mainlp/CrossRE)
- **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345)

### Dataset Summary
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes 
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and 
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL, 
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and 
RELATED-TO.

For details, see the paper: https://arxiv.org/abs/2210.09345

### 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 CrossRE is in English (BCP-47 en)

## Dataset Structure

### Data Instances

#### news
- **Size of downloaded dataset files:** 0.24 MB
- **Size of the generated dataset:** 0.22 MB

An example of 'train' looks as follows:
```python
{
  "doc_key": "news-train-1", 
  "sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], 
  "ner": [
    {"id-start": 0, "id-end": 0, "entity-type": "organisation"}, 
    {"id-start": 2, "id-end": 3, "entity-type": "misc"}, 
    {"id-start": 6, "id-end": 7, "entity-type": "misc"}
  ], 
  "relations": [
    {"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False}, 
    {"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False}, 
    {"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
  ]
}
```

#### politics
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.65 MB

An example of 'train' looks as follows:
```python
{
  "doc_key": "politics-train-1", 
  "sentence": ["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": [
    {"id-start": 8, "id-end": 9, "entity-type": "politicalparty"}, 
    {"id-start": 15, "id-end": 20, "entity-type": "politicalparty"}, 
    {"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
  ], 
  "relations": [
    {"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}, 
    {"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
  ]
}
```

#### science
- **Size of downloaded dataset files:** 0.59 MB
- **Size of the generated dataset:** 0.54 MB

An example of 'train' looks as follows:
```python
{
  "doc_key": "science-train-1", 
  "sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."], 
  "ner": [
    {"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"}, 
    {"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"}, 
    {"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
  ], 
  "relations": []
}
```

#### music
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.64 MB

An example of 'train' looks as follows:
```python
{
  "doc_key": "music-train-1", 
  "sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."], 
  "ner": [
    {"id-start": 4, "id-end": 6, "entity-type": "location"}, 
    {"id-start": 13, "id-end": 17, "entity-type": "event"}
  ], 
  "relations": [
    {"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
  ]
}
```

#### literature
- **Size of downloaded dataset files:** 0.64 MB
- **Size of the generated dataset:** 0.57 MB

An example of 'train' looks as follows:
```python
{
  "doc_key": "literature-train-1", 
  "sentence": ["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": [
    {"id-start": 7, "id-end": 9, "entity-type": "person"}, 
    {"id-start": 12, "id-end": 13, "entity-type": "country"}, 
    {"id-start": 17, "id-end": 18, "entity-type": "writer"}, 
    {"id-start": 20, "id-end": 20, "entity-type": "writer"}, 
    {"id-start": 26, "id-end": 27, "entity-type": "writer"}, 
    {"id-start": 29, "id-end": 29, "entity-type": "writer"}, 
    {"id-start": 33, "id-end": 33, "entity-type": "country"}, 
    {"id-start": 35, "id-end": 35, "entity-type": "country"}, 
    {"id-start": 38, "id-end": 38, "entity-type": "misc"}, 
    {"id-start": 45, "id-end": 46, "entity-type": "person"}, 
    {"id-start": 49, "id-end": 50, "entity-type": "misc"}, 
    {"id-start": 55, "id-end": 55, "entity-type": "person"}
  ], 
  "relations": [
    {"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False}, 
    {"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False}, 
    {"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False}, 
    {"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
  ]
}
```

#### ai
- **Size of downloaded dataset files:** 0.51 MB
- **Size of the generated dataset:** 0.46 MB

An example of 'train' looks as follows:
```python
{
  "doc_key": "ai-train-1", 
  "sentence": ["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": [
    {"id-start": 3, "id-end": 5, "entity-type": "product"}, 
    {"id-start": 10, "id-end": 11, "entity-type": "field"}, 
    {"id-start": 13, "id-end": 14, "entity-type": "task"}, 
    {"id-start": 16, "id-end": 17, "entity-type": "task"}, 
    {"id-start": 21, "id-end": 23, "entity-type": "task"}, 
    {"id-start": 26, "id-end": 27, "entity-type": "field"}, 
    {"id-start": 28, "id-end": 29, "entity-type": "researcher"}, 
    {"id-start": 31, "id-end": 32, "entity-type": "researcher"}, 
    {"id-start": 34, "id-end": 35, "entity-type": "researcher"}, 
    {"id-start": 37, "id-end": 38, "entity-type": "researcher"}, 
    {"id-start": 40, "id-end": 41, "entity-type": "researcher"}, 
    {"id-start": 43, "id-end": 44, "entity-type": "researcher"}, 
    {"id-start": 46, "id-end": 47, "entity-type": "researcher"}, 
    {"id-start": 49, "id-end": 50, "entity-type": "researcher"}
  ], 
  "relations": [
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, 
    {"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
  ]
}
```

### Data Fields

The data fields are the same among all splits.
- `doc_key`: the instance id of this sentence, a `string` feature.
- `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
- `ner`: the list of named entities in this sentence, a `list` of `dict` features.
  - `id-start`: the start index of the entity, a `int` feature.
  - `id-end`: the end index of the entity, a `int` feature.
  - `entity-type`: the type of the entity, a `string` feature.
- `relations`: the list of relations in this sentence, a `list` of `dict` features.
  - `id_1-start`: the start index of the first entity, a `int` feature.
  - `id_1-end`: the end index of the first entity, a `int` feature.
  - `id_2-start`: the start index of the second entity, a `int` feature.
  - `id_2-end`: the end index of the second entity, a `int` feature.
  - `relation-type`: the type of the relation, a `string` feature.
  - `Exp`: the explanation of the relation type assigned, a `string` feature.
  - `Un`: uncertainty of the annotator, a `bool` feature.
  - `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature.

### Data Splits
#### Sentences
|              | Train   | Dev     | Test    | Total   |
|--------------|---------|---------|---------|---------|
| news         | 164     | 350     | 400     | 914     |
| politics     | 101     | 350     | 400     | 851     |
| science      | 103     | 351     | 400     | 854     |
| music        | 100     | 350     | 399     | 849     |
| literature   | 100     | 400     | 416     | 916     |
| ai           | 100     | 350     | 431     | 881     |
| ------------ | ------- | ------- | ------- | ------- |
| total        | 668     | 2,151   | 2,46    | 5,265   |

#### Relations
|              | Train   | Dev     | Test    | Total   |
|--------------|---------|---------|---------|---------|
| news         | 175     | 300     | 396     | 871     |
| politics     | 502     | 1,616   | 1,831   | 3,949   |
| science      | 355     | 1,340   | 1,393   | 3,088   |
| music        | 496     | 1,861   | 2,333   | 4,690   |
| literature   | 397     | 1,539   | 1,591   | 3,527   |
| ai           | 350     | 1,006   | 1,127   | 2,483   |
| ------------ | ------- | ------- | ------- | ------- |
| total        | 2,275   | 7,662   | 8,671   | 18,608  |

## 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

```
@inproceedings{bassignana-plank-2022-crossre,
    title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
    author = "Bassignana, Elisa and Plank, Barbara",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    year = "2022",
    publisher = "Association for Computational Linguistics"
}
```

### Contributions

Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
task_categories:text-classification task_ids:multi-class-classification annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|cross_ner language:en cross domain ai news music literature politics science arxiv:2210.09345 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|cross_ner"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "CrossRE is a cross-domain dataset for relation extraction", "tags": ["cross domain", "ai", "news", "music", "literature", "politics", "science"], "dataset_info": [{"config_name": "ai", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 62411, "num_examples": 100}, {"name": "validation", "num_bytes": 183717, "num_examples": 350}, {"name": "test", "num_bytes": 217353, "num_examples": 431}], "download_size": 508107, "dataset_size": 463481}, {"config_name": "literature", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 62699, "num_examples": 100}, {"name": "validation", "num_bytes": 246214, "num_examples": 400}, {"name": "test", "num_bytes": 264450, "num_examples": 416}], "download_size": 635130, "dataset_size": 573363}, {"config_name": "music", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 69846, "num_examples": 100}, {"name": "validation", "num_bytes": 261497, "num_examples": 350}, {"name": "test", "num_bytes": 312165, "num_examples": 399}], "download_size": 726956, "dataset_size": 643508}, {"config_name": "news", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 49102, "num_examples": 164}, {"name": "validation", "num_bytes": 77952, "num_examples": 350}, {"name": "test", "num_bytes": 96301, "num_examples": 400}], "download_size": 239763, "dataset_size": 223355}, {"config_name": "politics", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 76004, "num_examples": 101}, {"name": "validation", "num_bytes": 277633, "num_examples": 350}, {"name": "test", "num_bytes": 295294, "num_examples": 400}], "download_size": 726427, "dataset_size": 648931}, {"config_name": "science", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 63876, "num_examples": 103}, {"name": "validation", "num_bytes": 224402, "num_examples": 351}, {"name": "test", "num_bytes": 249075, "num_examples": 400}], "download_size": 594058, "dataset_size": 537353}]}
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|cross_ner #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2210.09345 #region-us 

Dataset Card for CrossRE
========================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ 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
	+ Contributions


Dataset Description
-------------------


* Repository: CrossRE
* Paper: CrossRE: A Cross-Domain Dataset for Relation Extraction


### Dataset Summary


CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.


For details, see the paper: URL


### Supported Tasks and Leaderboards


### Languages


The language data in CrossRE is in English (BCP-47 en)


Dataset Structure
-----------------


### Data Instances


#### news


* Size of downloaded dataset files: 0.24 MB
* Size of the generated dataset: 0.22 MB


An example of 'train' looks as follows:


#### politics


* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.65 MB


An example of 'train' looks as follows:


#### science


* Size of downloaded dataset files: 0.59 MB
* Size of the generated dataset: 0.54 MB


An example of 'train' looks as follows:


#### music


* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.64 MB


An example of 'train' looks as follows:


#### literature


* Size of downloaded dataset files: 0.64 MB
* Size of the generated dataset: 0.57 MB


An example of 'train' looks as follows:


#### ai


* Size of downloaded dataset files: 0.51 MB
* Size of the generated dataset: 0.46 MB


An example of 'train' looks as follows:


### Data Fields


The data fields are the same among all splits.


* 'doc\_key': the instance id of this sentence, a 'string' feature.
* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.
* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.
	+ 'id-start': the start index of the entity, a 'int' feature.
	+ 'id-end': the end index of the entity, a 'int' feature.
	+ 'entity-type': the type of the entity, a 'string' feature.
* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.
	+ 'id\_1-start': the start index of the first entity, a 'int' feature.
	+ 'id\_1-end': the end index of the first entity, a 'int' feature.
	+ 'id\_2-start': the start index of the second entity, a 'int' feature.
	+ 'id\_2-end': the end index of the second entity, a 'int' feature.
	+ 'relation-type': the type of the relation, a 'string' feature.
	+ 'Exp': the explanation of the relation type assigned, a 'string' feature.
	+ 'Un': uncertainty of the annotator, a 'bool' feature.
	+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature.


### Data Splits


#### Sentences



#### Relations



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


### Contributions


Thanks to @phucdev for adding this dataset.



### Dataset Summary


CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.


For details, see the paper: URL ### Supported Tasks and Leaderboards ### Languages


The language data in CrossRE is in English (BCP-47 en)


Dataset Structure
----------------- ### Data Instances #### news


* Size of downloaded dataset files: 0.24 MB
* Size of the generated dataset: 0.22 MB


An example of 'train' looks as follows: #### politics


* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.65 MB


An example of 'train' looks as follows: #### science


* Size of downloaded dataset files: 0.59 MB
* Size of the generated dataset: 0.54 MB


An example of 'train' looks as follows: #### music


* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.64 MB


An example of 'train' looks as follows: #### literature


* Size of downloaded dataset files: 0.64 MB
* Size of the generated dataset: 0.57 MB


An example of 'train' looks as follows: #### ai


* Size of downloaded dataset files: 0.51 MB
* Size of the generated dataset: 0.46 MB


An example of 'train' looks as follows: ### Data Fields


The data fields are the same among all splits.


* 'doc\_key': the instance id of this sentence, a 'string' feature.
* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.
* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.
	+ 'id-start': the start index of the entity, a 'int' feature.
	+ 'id-end': the end index of the entity, a 'int' feature.
	+ 'entity-type': the type of the entity, a 'string' feature.
* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.
	+ 'id\_1-start': the start index of the first entity, a 'int' feature.
	+ 'id\_1-end': the end index of the first entity, a 'int' feature.
	+ 'id\_2-start': the start index of the second entity, a 'int' feature.
	+ 'id\_2-end': the end index of the second entity, a 'int' feature.
	+ 'relation-type': the type of the relation, a 'string' feature.
	+ 'Exp': the explanation of the relation type assigned, a 'string' feature.
	+ 'Un': uncertainty of the annotator, a 'bool' feature.
	+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature. ### Data Splits #### Sentences #### Relations



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 ### Contributions


Thanks to @phucdev for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|cross_ner #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2210.09345 #region-us 
 ### Dataset Summary


CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.


For details, see the paper: URL ### Supported Tasks and Leaderboards ### Languages


The language data in CrossRE is in English (BCP-47 en)


Dataset Structure
----------------- ### Data Instances #### news


* Size of downloaded dataset files: 0.24 MB
* Size of the generated dataset: 0.22 MB


An example of 'train' looks as follows: #### politics


* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.65 MB


An example of 'train' looks as follows: #### science


* Size of downloaded dataset files: 0.59 MB
* Size of the generated dataset: 0.54 MB


An example of 'train' looks as follows: #### music


* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.64 MB


An example of 'train' looks as follows: #### literature


* Size of downloaded dataset files: 0.64 MB
* Size of the generated dataset: 0.57 MB


An example of 'train' looks as follows: #### ai


* Size of downloaded dataset files: 0.51 MB
* Size of the generated dataset: 0.46 MB


An example of 'train' looks as follows: ### Data Fields


The data fields are the same among all splits.


* 'doc\_key': the instance id of this sentence, a 'string' feature.
* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.
* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.
	+ 'id-start': the start index of the entity, a 'int' feature.
	+ 'id-end': the end index of the entity, a 'int' feature.
	+ 'entity-type': the type of the entity, a 'string' feature.
* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.
	+ 'id\_1-start': the start index of the first entity, a 'int' feature.
	+ 'id\_1-end': the end index of the first entity, a 'int' feature.
	+ 'id\_2-start': the start index of the second entity, a 'int' feature.
	+ 'id\_2-end': the end index of the second entity, a 'int' feature.
	+ 'relation-type': the type of the relation, a 'string' feature.
	+ 'Exp': the explanation of the relation type assigned, a 'string' feature.
	+ 'Un': uncertainty of the annotator, a 'bool' feature.
	+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature. ### Data Splits #### Sentences #### Relations



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 ### Contributions


Thanks to @phucdev for adding this dataset.