relbert/lexical_relation_classification

---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- n<1K
pretty_name: Lexical Relation Classification
---

# Dataset Card for "relbert/lexical_relation_classification"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/P19-1169/](https://aclanthology.org/P19-1169/)
- **Dataset:** Lexical Relation Classification

### Dataset Summary
Five different datasets (`BLESS`, `CogALexV`, `EVALution`, `K&H+N`, `ROOT09`) for lexical relation classification used in [SphereRE](https://www.aclweb.org/anthology/P19-1169/).

### Dataset Summary
This dataset contains 5 different word analogy questions used in [Analogy Language Model](https://aclanthology.org/2021.acl-long.280/).

| name | train | validation | test |
|---------------|------:|-------:|-----:|
| `BLESS` | 18582 | 1327 | 6637 |
| `CogALexV` | 3054 | - | 4260 |
| `EVALution` | 5160 | 372 | 1846 |
| `K&H+N` | 40256 | 2876 | 14377 |
| `ROOT09` | 8933 | 638 | 3191 |

## Dataset Structure
### Data Instances
An example looks as follows.
```
{"head": "turtle", "tail": "live", "relation": "event"}
```
The `stem` and `tail` are the word pair and `relation` is the corresponding relation label.

### Citation Information
```
@inproceedings{wang-etal-2019-spherere,
title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings",
author = "Wang, Chengyu and
He, Xiaofeng and
Zhou, Aoying",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1169",
doi = "10.18653/v1/P19-1169",
pages = "1727--1737",
abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.",
}
```

### LICENSE
The LICENSE of all the resources are under [CC-BY-NC-4.0](./LICENSE). Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.

原始信息汇总

数据集概述

数据集名称

• 名称: Lexical Relation Classification

数据集描述

• 摘要: 包含五个不同的词汇关系分类数据集（BLESS, CogALexV, EVALution, K&H+N, ROOT09），用于词汇关系分类任务。
• 数据集结构:
  • 数据实例: 示例格式为 {"head": "turtle", "tail": "live", "relation": "event"}，其中 head 和 tail 表示词对，relation 表示相应的关联标签。

数据集详细信息

• 数据集大小: 小于1000个数据点。
• 语言: 英语。
• 许可证: 其他（CC-BY-NC-4.0），适用于学术目的或个人研究，限制商业使用。
• 多语言性: 单语。

数据集组成部分

• 组成部分:

  名称	训练集	验证集	测试集
  BLESS	18582	1327	6637
  CogALexV	3054	-	4260
  EVALution	5160	372	1846
  K&H+N	40256	2876	14377
  ROOT09	8933	638	3191

引用信息

• 引用:

  @inproceedings{wang-etal-2019-spherere, title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings", author = "Wang, Chengyu and He, Xiaofeng and Zhou, Aoying", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1169", doi = "10.18653/v1/P19-1169", pages = "1727--1737", abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.", }