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|
---
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language:
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- eng
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|
- fra
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|
- deu
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- spa
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- tur
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multilinguality:
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- multilingual
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configs:
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|
- config_name: en
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data_files:
|
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|
- split: train
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path: "RELX_distant_en.json"
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|
- config_name: fr
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|
data_files:
|
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|
- split: train
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|
|
path: "RELX_distant_fr.json"
|
|
|
- config_name: de
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|
data_files:
|
|
|
- split: train
|
|
|
path: "RELX_distant_de.json"
|
|
|
- config_name: es
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|
|
data_files:
|
|
|
- split: train
|
|
|
path: "RELX_distant_es.json"
|
|
|
- config_name: tr
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|
data_files:
|
|
|
- split: train
|
|
|
path: "RELX_distant_tr.json"
|
|
|
---
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|
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|
> [!NOTE]
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> Dataset origin: https://github.com/boun-tabi/RELX
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|
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## RELX-Distant
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|
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|
This dataset is gathered from Wikipedia and Wikidata.
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|
The process is as follows:
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|
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|
1. The Wikipedia dumps for the corresponding languages are downloaded and converted into raw documents with Wikipedia hyperlinks in entities.
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|
2. The raw documents are split into sentences with spaCy (Honnibal and Montani, 2017), and all hyperlinks are converted to their corresponding Wikidata IDs.
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|
3. Sentences that include entity pairs with Wikidata relations (Vrandečić and Krötzsch, 2014) are collected. We filter and combine some of the relations and propose RELX-Distant whose statistics can be seen in the table below.
|
|
|
|
|
|
| **Language** | **Number of Sentences** |
|
|
|
|--------------|-------------------------|
|
|
|
| English | 815689 |
|
|
|
| French | 652842 |
|
|
|
| German | 652062 |
|
|
|
| Spanish | 397875 |
|
|
|
| Turkish | 57114 |
|
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|
|
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|
## Citation
|
|
|
|
|
|
```
|
|
|
@inproceedings{koksal-ozgur-2020-relx,
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|
|
title = "The {RELX} Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification",
|
|
|
author = {K{\"o}ksal, Abdullatif and
|
|
|
{\"O}zg{\"u}r, Arzucan},
|
|
|
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
|
|
|
month = nov,
|
|
|
year = "2020",
|
|
|
address = "Online",
|
|
|
publisher = "Association for Computational Linguistics",
|
|
|
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.32",
|
|
|
doi = "10.18653/v1/2020.findings-emnlp.32",
|
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|
pages = "340--350",
|
|
|
}
|
|
|
``` |
