Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
German
Size:
10K - 100K
Tags:
GermEval
License:
File size: 2,847 Bytes
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---
dataset_info:
features:
- name: id
dtype: int64
- name: source
dtype: string
- name: source_date
dtype: string
- name: tokens
sequence: string
- name: ner_label
sequence: int64
- name: ner_tag
sequence: string
- name: nested_ner_label
sequence: int64
- name: nested_ner_tag
sequence: string
splits:
- name: train
num_bytes: 18729899
num_examples: 24002
- name: validation
num_bytes: 1721290
num_examples: 2200
- name: test
num_bytes: 3993690
num_examples: 5100
download_size: 4900445
dataset_size: 24444879
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: mit
task_categories:
- token-classification
language:
- de
tags:
- GermEval
pretty_name: GermEval 2014 NER challenge dataset
size_categories:
- 1M<n<10M
---
# GermEval 14 NER dataset
This dataset includes the actual NER tags (B-PER, B-LOC, etc.) besides the labels (0, 1, 2, ...) and requires no code execution when loading. Structured as follow
```
DatasetDict({
train: Dataset({
features: ['id', 'source', 'source_date', 'tokens', 'ner_label', 'ner_tag', 'nested_ner_label', 'nested_ner_tag'],
num_rows: 24002
})
validation: Dataset({
features: ['id', 'source', 'source_date', 'tokens', 'ner_label', 'ner_tag', 'nested_ner_label', 'nested_ner_tag'],
num_rows: 2200
})
test: Dataset({
features: ['id', 'source', 'source_date', 'tokens', 'ner_label', 'ner_tag', 'nested_ner_label', 'nested_ner_tag'],
num_rows: 5100
})
})
```
# Citation
Based on the data from the GermEval14 NER challenge, please cite the original authors when using this dataset in research:
```
@article{benikovaGermEval2014Named,
title = {{{GermEval}} 2014 {{Named Entity Recognition Shared Task}}: {{Companion Paper}}},
author = {Benikova, Darina and Biemann, Chris and Kisselew, Max and Pado, Sebastian},
abstract = {This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-theart machine learning methods, combined with some knowledge-based features in hybrid systems.},
langid = {english},
}
``` |