
# Dataset Card for "tner/wikineural"

## Dataset Description

- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/)
- **Dataset:** WikiNeural
- **Domain:** Wikipedia
- **Number of Entity:** 16


### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`

## Dataset Structure

### Data Instances
An example of `train` of `de` looks as follows.

```
{
    'tokens': [ "Dieses", "wiederum", "basierte", "auf", "dem", "gleichnamigen", "Roman", "von", "Noël", "Calef", "." ],
    'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0 ]
}
```

### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-MISC": 31,
"I-MISC": 32
}
```

### Data Splits

| language   |   train |   validation |   test |
|:-----------|--------:|-------------:|-------:|
| de         |   98640 |        12330 |  12372 |
| en         |   92720 |        11590 |  11597 |
| es         |   76320 |         9540 |   9618 |
| fr         |  100800 |        12600 |  12678 |
| it         |   88400 |        11050 |  11069 |
| nl         |   83680 |        10460 |  10547 |
| pl         |  108160 |        13520 |  13585 |
| pt         |   80560 |        10070 |  10160 |
| ru         |   92320 |        11540 |  11580 |

### Citation Information

```
@inproceedings{tedeschi-etal-2021-wikineural-combined,
    title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
    author = "Tedeschi, Simone  and
      Maiorca, Valentino  and
      Campolungo, Niccol{\`o}  and
      Cecconi, Francesco  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.215",
    doi = "10.18653/v1/2021.findings-emnlp.215",
    pages = "2521--2533",
    abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
```
task_categories:token-classification task_ids:named-entity-recognition multilinguality:multilingual size_categories:10K<100k language:de language:en language:es language:fr language:it language:nl language:pl language:pt language:ru region:us
{"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "multilinguality": ["multilingual"], "size_categories": ["10K<100k"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WikiNeural"}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-multilingual #size_categories-10K<100k #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #region-us 

Dataset Card for "tner/wikineural"
==================================


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


* Repository: T-NER
* Paper: URL
* Dataset: WikiNeural
* Domain: Wikipedia
* Number of Entity: 16


### Dataset Summary


WikiAnn NER dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC'


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


### Data Instances


An example of 'train' of 'de' looks as follows.


### Label ID


The label2id dictionary can be found at here.


### Data Splits




### Dataset Summary


WikiAnn NER dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC'


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


An example of 'train' of 'de' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-multilingual #size_categories-10K<100k #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #region-us 
 ### Dataset Summary


WikiAnn NER dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC'


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


An example of 'train' of 'de' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-multilingual #size_categories-10K<100k #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #region-us 
### Dataset Summary


WikiAnn NER dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC'


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


An example of 'train' of 'de' looks as follows.### Label ID


The label2id dictionary can be found at here.### Data Splits