---
language:
- de
---

# HisGermaNER: NER Datasets for Historical German

In this repository we release another NER dataset from historical German newspapers.

## Newspaper corpus

In the first release of our dataset, we select 11 newspapers from 1710 to 1840 from the Austrian National Library (ONB), resulting in 100 pages:

| Year | ONB ID             | Newspaper                        | URL                                                                      | Pages |
| ---- | ------------------ | -------------------------------- | ------------------------------------------------------------------------ | ----- |
| 1720 | `ONB_wrz_17200511` | Wiener Zeitung                   | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=wrz&datum=17200511) | 10    |
| 1730 | `ONB_wrz_17300603` | Wiener Zeitung                   | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=wrz&datum=17300603) | 14    |
| 1740 | `ONB_wrz_17401109` | Wiener Zeitung                   | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=wrz&datum=17401109) | 12    |
| 1770 | `ONB_rpr_17700517` | Reichspostreuter                 | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=rpr&datum=17700517) | 4     |
| 1780 | `ONB_wrz_17800701` | Wiener Zeitung                   | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=wrz&datum=17800701) | 24    |
| 1790 | `ONB_pre_17901030` | Preßburger Zeitung               | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=pre&datum=17901030) | 12    |
| 1800 | `ONB_ibs_18000322` | Intelligenzblatt von Salzburg    | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=ibs&datum=18000322) | 8     |
| 1810 | `ONB_mgs_18100508` | Morgenblatt für gebildete Stände | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=mgs&datum=18100508) | 4     |
| 1820 | `ONB_wan_18200824` | Der Wanderer                     | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=wan&datum=18200824) | 4     |
| 1830 | `ONB_ild_18300713` | Das Inland                       | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=ild&datum=18300713) | 4     |
| 1840 | `ONB_hum_18400625` | Der Humorist                     | [Viewer](https://anno.onb.ac.at/cgi-content/anno?aid=hum&datum=18400625) | 4     |

## Data Workflow

In the first step, we obtain original scans from ONB for our selected newspapers. In the second step, we perform OCR using [Transkribus](https://readcoop.eu/de/transkribus/).

We use the [Transkribus print M1](https://readcoop.eu/model/transkribus-print-multi-language-dutch-german-english-finnish-french-swedish-etc/) model for performing OCR.
Note: we experimented with an existing NewsEye model, but the print M1 model is newer and led to better performance in our preliminary experiments.

Only layout hints/fixes were made in Transkribus. So no OCR corrections or normalizations were performed.

We export plain text of all newspaper pages into plain text format and perform normalization of hyphenation and the `=` character.
After normalization we tokenize the plain text newspaper pages using the `PreTokenizer` of the [hmBERT](https://huggingface.co/hmbert) model.

After pre-tokenization we import the corpus into Argilla to start the annotation of named entities.
Note: We perform annotation at page/document-level. Thus, no sentence segmentation is needed and performed.
In the annotation process we also manually annotate sentence boundaries using a special `EOS` tag.

The dataset is exported into an CoNLL-like format after the annotation process.
The `EOS` tag is removed and the information of an potential end of sentence is stored in a special column.

## Annotation Guidelines

We use the same NE's (`PER`, `LOC` and `ORG`) and annotation guideline as used in the awesome [Europeana NER Corpora](https://github.com/cneud/ner-corpora).

Furthermore, we introduced some specific rules for annotations:

* `PER`: We include e.g. `Kaiser`, `Lord`, `Cardinal` or `Graf` in the NE, but not `Herr`, `Fräulein`, `General` or rank/grades.
* `LOC`: We excluded `Königreich` from the NE.

## Dataset Format

Our dataset format is inspired by the [HIPE-2022 Shared Task](https://github.com/hipe-eval/HIPE-2022-data?tab=readme-ov-file#hipe-format-and-tagging-scheme).
Here's an example of an annotated document:

```txt
TOKEN	NE-COARSE-LIT	MISC

-DOCSTART-	O

# onb:id = ONB_wrz_17800701
# onb:image_link = https://anno.onb.ac.at/cgi-content/anno?aid=wrz&datum=17800701&seite=12
# onb:page_nr = 12
# onb:publication_year_str = 17800701
den	O	_
Pöbel	O	_
noch	O	_
mehr	O	_
in	O	_
Harnisch	O	_
.	O	EndOfSentence
Sie	O	_
legten	O	_
sogleich	O	_
```

Note: we include a `-DOCSTART-` information to e.g. allow document-level features for NER as proposed in the [FLERT](https://arxiv.org/abs/2011.06993) paper.

## Dataset Splits

For training powerful NER models on the dataset, we manually splitted the dataset into training, development and test splits.