DFKI-SLT/kbp37

---
annotations_creators:
- other
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: KBP37 is an English Relation Classification dataset
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
tags:
- relation extraction
task_categories:
- text-classification
task_ids:
- multi-class-classification
dataset_info:
- config_name: kbp37
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: relation
dtype:
class_label:
names:
'0': no_relation
'1': org:alternate_names(e1,e2)
'2': org:alternate_names(e2,e1)
'3': org:city_of_headquarters(e1,e2)
'4': org:city_of_headquarters(e2,e1)
'5': org:country_of_headquarters(e1,e2)
'6': org:country_of_headquarters(e2,e1)
'7': org:founded(e1,e2)
'8': org:founded(e2,e1)
'9': org:founded_by(e1,e2)
'10': org:founded_by(e2,e1)
'11': org:members(e1,e2)
'12': org:members(e2,e1)
'13': org:stateorprovince_of_headquarters(e1,e2)
'14': org:stateorprovince_of_headquarters(e2,e1)
'15': org:subsidiaries(e1,e2)
'16': org:subsidiaries(e2,e1)
'17': org:top_members/employees(e1,e2)
'18': org:top_members/employees(e2,e1)
'19': per:alternate_names(e1,e2)
'20': per:alternate_names(e2,e1)
'21': per:cities_of_residence(e1,e2)
'22': per:cities_of_residence(e2,e1)
'23': per:countries_of_residence(e1,e2)
'24': per:countries_of_residence(e2,e1)
'25': per:country_of_birth(e1,e2)
'26': per:country_of_birth(e2,e1)
'27': per:employee_of(e1,e2)
'28': per:employee_of(e2,e1)
'29': per:origin(e1,e2)
'30': per:origin(e2,e1)
'31': per:spouse(e1,e2)
'32': per:spouse(e2,e1)
'33': per:stateorprovinces_of_residence(e1,e2)
'34': per:stateorprovinces_of_residence(e2,e1)
'35': per:title(e1,e2)
'36': per:title(e2,e1)
splits:
- name: train
num_bytes: 3570626
num_examples: 15917
- name: validation
num_bytes: 388935
num_examples: 1724
- name: test
num_bytes: 762806
num_examples: 3405
download_size: 5106673
dataset_size: 4722367
- config_name: kbp37_formatted
features:
- name: id
dtype: string
- name: token
sequence: string
- name: e1_start
dtype: int32
- name: e1_end
dtype: int32
- name: e2_start
dtype: int32
- name: e2_end
dtype: int32
- name: relation
dtype:
class_label:
names:
'0': no_relation
'1': org:alternate_names(e1,e2)
'2': org:alternate_names(e2,e1)
'3': org:city_of_headquarters(e1,e2)
'4': org:city_of_headquarters(e2,e1)
'5': org:country_of_headquarters(e1,e2)
'6': org:country_of_headquarters(e2,e1)
'7': org:founded(e1,e2)
'8': org:founded(e2,e1)
'9': org:founded_by(e1,e2)
'10': org:founded_by(e2,e1)
'11': org:members(e1,e2)
'12': org:members(e2,e1)
'13': org:stateorprovince_of_headquarters(e1,e2)
'14': org:stateorprovince_of_headquarters(e2,e1)
'15': org:subsidiaries(e1,e2)
'16': org:subsidiaries(e2,e1)
'17': org:top_members/employees(e1,e2)
'18': org:top_members/employees(e2,e1)
'19': per:alternate_names(e1,e2)
'20': per:alternate_names(e2,e1)
'21': per:cities_of_residence(e1,e2)
'22': per:cities_of_residence(e2,e1)
'23': per:countries_of_residence(e1,e2)
'24': per:countries_of_residence(e2,e1)
'25': per:country_of_birth(e1,e2)
'26': per:country_of_birth(e2,e1)
'27': per:employee_of(e1,e2)
'28': per:employee_of(e2,e1)
'29': per:origin(e1,e2)
'30': per:origin(e2,e1)
'31': per:spouse(e1,e2)
'32': per:spouse(e2,e1)
'33': per:stateorprovinces_of_residence(e1,e2)
'34': per:stateorprovinces_of_residence(e2,e1)
'35': per:title(e1,e2)
'36': per:title(e2,e1)
splits:
- name: train
num_bytes: 4943394
num_examples: 15807
- name: validation
num_bytes: 539197
num_examples: 1714
- name: test
num_bytes: 1055918
num_examples: 3379
download_size: 5106673
dataset_size: 6581345
---
# Dataset Card for "kbp37"
## 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
- **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Repository:** [kbp37](https://github.com/zhangdongxu/kbp37)
- **Paper:** [Relation Classification via Recurrent Neural Network](https://arxiv.org/abs/1508.01006)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 5.11 MB
- **Size of the generated dataset:** 6.58 MB

### Dataset Summary
KBP37 is a revision of MIML-RE annotation dataset, provided by Gabor Angeli et al. (2014). They use both the 2010 and
2013 KBP official document collections, as well as a July 2013 dump of Wikipedia as the text corpus for annotation.
There are 33811 sentences been annotated. Zhang and Wang made several refinements:
1. They add direction to the relation names, e.g. '`per:employee_of`' is split into '`per:employee of(e1,e2)`'
and '`per:employee of(e2,e1)`'. They also replace '`org:parents`' with '`org:subsidiaries`' and replace
'`org:member of’ with '`org:member`' (by their reverse directions).
2. They discard low frequency relations such that both directions of each relation occur more than 100 times in the
dataset.

KBP37 contains 18 directional relations and an additional '`no_relation`' relation, resulting in 37 relation classes.

Note:
- There is a formatted version that you can load with `datasets.load_dataset('kbp37', name='kbp37_formatted')`. This version is tokenized with `str.split()` and
provides entities as offsets instead of being enclosed by xml tags. It discards some examples, however, that are invalid in the original dataset and lead
to entity offset errors, e.g. example train/1276.

### 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 KBP37 is in English (BCP-47 en)

## Dataset Structure

### Data Instances

#### kbp37
- **Size of downloaded dataset files:** 5.11 MB
- **Size of the generated dataset:** 4.7 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"sentence": "<e1> Thom Yorke </e1> of <e2> Radiohead </e2> has included the + for many of his signature distortion sounds using a variety of guitars to achieve various tonal options .",
"relation": 27
}
```

#### kbp37_formatted
- **Size of downloaded dataset files:** 5.11 MB
- **Size of the generated dataset:** 6.58 MB
An example of 'train' looks as follows:
```json
{
"id": "1",
"token": ["Leland", "High", "School", "is", "a", "public", "high", "school", "located", "in", "the", "Almaden", "Valley", "in", "San", "Jose", "California", "USA", "in", "the", "San", "Jose", "Unified", "School", "District", "."],
"e1_start": 0,
"e1_end": 3,
"e2_start": 14,
"e2_end": 16,
"relation": 3
}
```

### Data Fields

#### kbp37
- `id`: the instance id of this sentence, a `string` feature.
- `sentence`: the sentence, a `string` features.
- `relation`: the relation label of this instance, an `int` classification label.

```python
{"no_relation": 0, "org:alternate_names(e1,e2)": 1, "org:alternate_names(e2,e1)": 2, "org:city_of_headquarters(e1,e2)": 3, "org:city_of_headquarters(e2,e1)": 4, "org:country_of_headquarters(e1,e2)": 5, "org:country_of_headquarters(e2,e1)": 6, "org:founded(e1,e2)": 7, "org:founded(e2,e1)": 8, "org:founded_by(e1,e2)": 9, "org:founded_by(e2,e1)": 10, "org:members(e1,e2)": 11, "org:members(e2,e1)": 12, "org:stateorprovince_of_headquarters(e1,e2)": 13, "org:stateorprovince_of_headquarters(e2,e1)": 14, "org:subsidiaries(e1,e2)": 15, "org:subsidiaries(e2,e1)": 16, "org:top_members/employees(e1,e2)": 17, "org:top_members/employees(e2,e1)": 18, "per:alternate_names(e1,e2)": 19, "per:alternate_names(e2,e1)": 20, "per:cities_of_residence(e1,e2)": 21, "per:cities_of_residence(e2,e1)": 22, "per:countries_of_residence(e1,e2)": 23, "per:countries_of_residence(e2,e1)": 24, "per:country_of_birth(e1,e2)": 25, "per:country_of_birth(e2,e1)": 26, "per:employee_of(e1,e2)": 27, "per:employee_of(e2,e1)": 28, "per:origin(e1,e2)": 29, "per:origin(e2,e1)": 30, "per:spouse(e1,e2)": 31, "per:spouse(e2,e1)": 32, "per:stateorprovinces_of_residence(e1,e2)": 33, "per:stateorprovinces_of_residence(e2,e1)": 34, "per:title(e1,e2)": 35, "per:title(e2,e1)": 36}
```

#### kbp37_formatted
- `id`: the instance id of this sentence, a `string` feature.
- `token`: the list of tokens of this sentence, using `str.split()`, a `list` of `string` features.
- `e1_start`: the 0-based index of the start token of the first argument', an `int` feature.
- `e1_end`: the 0-based index of the end token of the first argument, exclusive, an `int` feature.
- `e2_start`: the 0-based index of the start token of the second argument, an `int` feature.
- `e2_end`: the 0-based index of the end token of the second argument, exclusive, an `int` feature.
- `relation`: the relation label of this instance, an `int` classification label (same as `'kbp37''`).

### Data Splits

| | Train | Dev | Test |
|-------|-------|------|------|
| kbp37 | 15917 | 1724 | 3405 |
| kbp37_formatted | 15807 | 1714 | 3379 |

## 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{DBLP:journals/corr/ZhangW15a,
author = {Dongxu Zhang and
Dong Wang},
title = {Relation Classification via Recurrent Neural Network},
journal = {CoRR},
volume = {abs/1508.01006},
year = {2015},
url = {http://arxiv.org/abs/1508.01006},
eprinttype = {arXiv},
eprint = {1508.01006},
timestamp = {Fri, 04 Nov 2022 18:37:50 +0100},
biburl = {https://dblp.org/rec/journals/corr/ZhangW15a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

### Contributions

Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

原始信息汇总

数据集概述

数据集名称

• 名称: KBP37
• 别名: kbp37_formatted

数据集属性

• 语言: 英语 (en)
• 多语言性: 单语
• 许可证: 其他
• 注释创建者: 其他
• 语言创建者: 发现
• 标签: 关系抽取
• 任务类别: 文本分类
• 任务ID: 多类分类

数据集大小

• 大小类别: 10K<n<100K
• 下载大小: 5.11 MB
• 生成数据集大小:
  • kbp37: 4.7 MB
  • kbp37_formatted: 6.58 MB

数据集结构

• 配置名称: kbp37, kbp37_formatted
• 特征:
  • kbp37:
    • id: 字符串
    • sentence: 字符串
    • relation: 分类标签（0-36）
  • kbp37_formatted:
    • id: 字符串
    • token: 字符串序列
    • e1_start: 整数
    • e1_end: 整数
    • e2_start: 整数
    • e2_end: 整数
    • relation: 分类标签（0-36）

数据分割

• 训练集:
  • kbp37: 15917 条
  • kbp37_formatted: 15807 条
• 验证集:
  • kbp37: 1724 条
  • kbp37_formatted: 1714 条
• 测试集:
  • kbp37: 3405 条
  • kbp37_formatted: 3379 条

数据集创建

• 来源数据: 使用2010和2013 KBP官方文档集合，以及2013年7月的维基百科转储作为文本语料库进行注释。
• 注释: 包含33811个句子被注释。
• 改进: 添加了关系名称的方向，并去除了低频关系，确保每个关系的两个方向在数据集中出现超过100次。

使用注意事项

• 格式化版本: 提供了一个格式化版本，使用str.split()进行分词，并提供实体作为偏移量，而不是使用XML标签包围。此版本排除了原始数据集中导致实体偏移错误的无效示例。