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27229_36603_2_3
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auf deutschem Boden
true
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4PER
der
false
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4PER
Männer
false
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3ORG
Männer Deutschlands
false
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4PER
ihm
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3ORG
deutschen
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3ORG
Publicum
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4PER
Privatleute
false
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2MISC
Aufklärung
false
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2MISC
Aufklärung
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19_27250_20
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4PER
Stifter des Christenthums
true
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14
2MISC
Aufklärung
false
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2MISC
Aufklärung
false
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2MISC
Aufklärung
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Aufklärung
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Aufklärung
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2MISC
aufgeklärten Menschen
false
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2MISC
Anhänger
false
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2MISC
blinden Glaubens
false
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3ORG
bürgerlichen Geschäften
false
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2MISC
heilige Schrift
false
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3ORG
Kirche
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143
3ORG
Dienern der Gerechtigkeit
false
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3ORG
Francorum
false
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0
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4PER
Prof
false
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14
22
4PER
Levrault
false
93
108
74
102
2MISC
Capitularien der Fränkischen
false
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2MISC
Konige
false
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3ORG
Fränkischen Konige
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4PER
Konige
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2MISC
Dissertatio de Capitularium Regum Francorum nomine, dignitate, auctoritate, et usu tam in rebus eccles.
true
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2MISC
quam politicis, nec non de eorundem collectionibus et editionibus, quam per modum praefationis collectioni Capitularium Regum Frane.
true
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48
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2MISC
Capitularien der Fränlischen Könige
false
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3ORG
bürgerliche Gesellschaften
false
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131
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4PER
Fürsten
false
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3ORG
Staate
false
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3ORG
Staat
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124
136
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3ORG
Staate
false
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137
108
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3ORG
christlichen Religion
false
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Publicisten
false
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3ORG
Allgemeine Concilien
false
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4PER
öffentlicher Professor
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4PER
öffentlichen Professor
true
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3ORG
bischöflichen Universität
false
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3ORG
Canonisten
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3ORG
Kirche
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3ORG
Concilien
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3ORG
Staat
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4PER
Fürst
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4PER
Fürst
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36738_162_27406_163
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5TIME
aus den letzten Jahren Marien Theresiens
true
165
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4PER
Fürst
false
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4PER
Beschützer der Kirchenverordnungen
false
196_27461_36766_197_27463_199
178
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139
4PER
Prinz Rohan⸗Rochefort Canonicus in Straßburg Deo duce, et Auspice Deipara, Praeside Franc.
true
202
198
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3ORG
Germaniae
false
27476_203
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4PER
Henricus IV. Imperator exercuit
true
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4PER
Gregorius VII.
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3ORG
imperio germanico
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coronae Gallicae
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3ORG
Deutschen
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1LOC
Deutschen
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3ORG
Frankreich
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224
210
194
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3ORG
Schweden
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229_27507_36786_230
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3ORG
neuen Lehrstuhle der Staaterechnungs Wissenschaft
true
233_27510_36790_234
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4PER
Hrn. Brand.
true
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4PER
Regenten
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4PER
Censoren
false
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4PER
Schriftsteller
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4PER
der
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3ORG
gesetzgebenden Macht
false
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2MISC
k. k. Verordnung für die Büchercensur
true
269
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3ORG
Staate
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273
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64
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3ORG
Staate
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3ORG
Staat
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3ORG
Staat
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3ORG
Staates
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3ORG
Staate
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3ORG
kirchliche
false
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3ORG
kirchlichen Religion
false
300_27598_36853_303
307
41
62
5TIME
am Sesttage des heil.
true
27612_321
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7
32
3ORG
Joseph Wolffischen Buchh.
true
328
321
0
12
4PER
Neuprofessen
false
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253
4PER
Domprediger
false
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4PER
wohlehrwurdigen Neuprofessen
false
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4PER
gewöhnlichen
false
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4PER
Schultheologen
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4PER
seinem himmlischen Vater
true
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4PER
die
false
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4PER
Neuprofessen
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4PER
heil
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Vätern
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4PER
die
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4PER
heil
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4PER
Väter
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3ORG
Neichsritterstifte Odenheim Speyerischen Bisthums
true
383
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4PER
Enderesischen
false
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2MISC
Enderesischen Schriften
false
385
348
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3ORG
Enderesischen Schriften
false
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4PER
Dompredigers
false
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4PER
Dorfpfarrers
false

OALZ/1788/Q1/NER

A named entity recognition system (NER) was trained on text extracted from Oberdeutsche Allgemeine Litteraturueitung (OALZ) of the first quarter (January, Febuary, March) of 1788. The scans from which text was extracted can be found at Bayerische Staatsbibliothek using the extraction strategy of the KEDiff project, which can be found at cborgelt/KEDiff.

Annotations

Each text passage was annotated in doccano by two or three annotators and their annotations were cleaned and merged into one dataset. For details on how this was done, see LelViLamp/kediff-doccano-postprocessing. In total, the text consists of about 1.7m characters. The resulting annotation datasets were published on the Hugging Face Hub. There are two versions of the dataset

  • union-dataset contains the texts split into chunks. This is how they were presented in the annotation application doccano and results from preprocessing step 5a.
  • merged-union-dataset does not retain this split. The text was merged into one long text and annotation indices were adapted in preprocessing step 5b.

Note that both these directories contain three equivalent datasets each:

  • a Huggingface/Arrow dataset, *
  • a CSV, * and
  • a JSONL file.

* The former two should be used together with the provided text.csv to catch the context of the annotation. The latter JSONL file contains the full text.

The following categories were included in the annotation process:

Tag Label Count Total Length Median Annotation Length Mean Annotation Length SD
EVENT Event 294 6,090 18 20.71 13.24
LOC Location 2,449 24,417 9 9.97 6.21
MISC Miscellaneous 2,585 50,654 14 19.60 19.63
ORG Organisation 2,479 34,693 11 13.99 9.33
PER Person 7,055 64,710 7 9.17 9.35
TIME Dates & Time 1,076 13,154 8 12.22 10.98

NER models

Based on the annotations above, six separate NER classifiers were trained, one for each label type. This was done in order to allow overlapping annotations. For example, in the passage "Dieses Projekt wurde an der Universität Salzburg durchgeführt", you would want to categorise "Universität Salzburg" as an organisation while also extracting "Salzburg" as a location. This would result in an annotation like this:

{
  "id": "example-42",
  "text": "Dieses Projekt wurde an der Universität Salzburg durchgeführt",
  "label": [[28, 49, "ORG"], [40, 49, "LOC"]]
}

Example entry in CSV and Huggingface dataset

annotation_id line_id start end label label_text merged
$n$ example-42 28 49 ORG Universität Salzburg ???
$n+1$ example-42 40 49 LOC Salzburg ???

The columns mean:

  • annotation_id was assigned internally by enumerating all annotations. This is not present in the JSONL format
  • line_id is the fragment of the subdivided text, as shown in doccano. Called id in the JSONL dataset.
  • start index of the first character that is annotated. Included, starts with 0.
  • end index of the last character that is annotated. Excluded, maximum value is len(respectiveText).
  • label indicates what the passage indicated by $[start, end)$ was annotated as.
  • label_text contains the text that is annotated by $[start, end)$. This is not present in the JSONL dataset as it can be inferred there.
  • merged indicates whether this annotation is the result of overlapping annotations of the same label. In that case, annotation_id contains the IDs of the individual annotations it was constructed of. This is not present in the JSONL dataset.

To achieve this overlap, each text passage must be run through all the classifiers individually and each classifier's results need to be combined. For details on how the training was done, see LelViLamp/kediff-ner-training.

The dbmdz/bert-base-historic-multilingual-cased tokeniser was used to create historical embeddings. Therefore, it is necessary to use that in order to use these NER models.

The models' performance measures are as follows:

Model Selected Epoch Checkpoint Validation Loss Precision Recall F1 Accuracy
EVENT 1 1393 .021957 .665233 .343066 .351528 .995700
LOC 1 1393 .033602 .829535 .803648 .814146 .990999
MISC 2 2786 .123994 .739221 .503677 .571298 968697
ORG 1 1393 .062769 .744259 .709738 .726212 .980288
PER 2 2786 .059186 .914037 .849048 .879070 .983253
TIME 1 1393 .016120 .866866 .724958 .783099 .994631

Acknowledgements

The data set and models were created in the project Kooperative Erschließung diffusen Wissens (KEDiff), funded by the State of Salzburg, Austria 🇦🇹, and carried out at Paris Lodron Universität Salzburg.

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