dmargutierrez/Babelscape-wikineural-joined

---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: lang
dtype: string
splits:
- name: train
num_bytes: 319328641
num_examples: 821600
- name: validation
num_bytes: 39434957
num_examples: 102700
- name: test
num_bytes: 39371980
num_examples: 103206
download_size: 139847318
dataset_size: 398135578
task_categories:
- token-classification
language:
- es
- en
- nl
- fr
- it
- ru
- pt
- pl
- de
pretty_name: Wikineural
tags:
- named-entity-recognition
- wikipedia
- machine-generation
---
# Dataset Card for "Babelscape-wikineural-joined"

This dataset is a merged version of [wikineural](https://huggingface.co/datasets/Babelscape/wikineural)

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

<pre><code>
@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",
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.",
}

</code></pre>

原始信息汇总

数据集概述

数据集名称

• 名称: Babelscape-wikineural-joined

数据集特征

• tokens: 字符串序列
• ner_tags: 整数序列
• lang: 字符串类型

数据集分割

• 训练集: 821600个样本，占用319328641字节
• 验证集: 102700个样本，占用39434957字节
• 测试集: 103206个样本，占用39371980字节

数据集大小

• 下载大小: 139847318字节
• 数据集总大小: 398135578字节

任务类别

• 标记分类

支持语言

• 西班牙语 (es)
• 英语 (en)
• 荷兰语 (nl)
• 法语 (fr)
• 意大利语 (it)
• 俄语 (ru)
• 葡萄牙语 (pt)
• 波兰语 (pl)
• 德语 (de)

数据集别名

• 别名: Wikineural

数据集标签

• 命名实体识别
• 维基百科
• 机器生成