Diderot & d’Alembert’s Encyclopédie: Gold Standard Corpus for Named Entity Recognition and Span Categorization Annotations

This repository contains a gold standard corpus for named entity recognition and span categorization annotations from Diderot & d’Alembert’s Encyclopédie entries.

Developed by: Ludovic Moncla, Katherine McDonough and Denis Vigier within the framework of the GEODE project.

Dataset and methodology

Tagset

• NC-Spatial: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. ville, la rivière, royaume.
• NP-Spatial: a proper noun identifying the name of a place (spatial named entities), e.g. France, Paris, la Chine.
• ENE-Spatial: nested spatial entity , e.g. ville de France, royaume de Naples, la mer Baltique.
• Relation: spatial relation, e.g. dans, sur, à 10 lieues de.
• Latlong: geographic coordinates, e.g. Long. 19. 49. lat. 43. 55. 44.
• NC-Person: a common noun that identifies a person (nominal spatial entity), e.g. roi, l'empereur, les auteurs.
• NP-Person: a proper noun identifying the name of a person (person named entities), e.g. Louis XIV, Pline, les Romains.
• ENE-Person: nested people entity, e.g. le czar Pierre, roi de Macédoine
• NP-Misc: a proper noun identifying entities not classified as spatial or person, e.g. l'Eglise, 1702, Pélasgique.
• ENE-Misc: nested named entity not classified as spatial or person, e.g. l'ordre de S. Jacques, la déclaration du 21 Mars 1671.
• Head: entry name
• Domain-Mark: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. Géographie, Geog., en Anatomie

Dataset overview

The Gold Standard dataset is composed of 2,200 paragraphs out of 2,001 Encyclopédie's entries randomly selected. All paragraphs were written in French and are distributed as follows among the Encyclopédie knowledge domains:

Knowledge domain	Paragraphs
Géographie	1,096
Histoire	259
Droit Jurisprudence	113
Physique	92
Métiers	92
Médecine	88
Philosophie	69
Histoire naturelle	65
Belles-lettres	65
Militaire	62
Commerce	48
Beaux-arts	44
Agriculture	36
Chasse	31
Religion	23
Musique	17

The spans/entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process. A train/val/test split was used. Validation and test sets are composed of 200 paragraphs each: 100 classified as 'Géographie' and 100 from another knowledge domain. The datasets have the following breakdown of tokens and spans/entities.

	Train	Validation	Test
Paragraphs	1,800	200	200
Tokens	132,398	14,959	13,881
NC-Spatial	3,252	358	355
NP-Spatial	4,707	464	519
ENE-Spatial	3,033	326	334
Relation	2,093	219	226
Latlong	553	66	72
NC-Person	1,378	132	133
NP-Person	1,599	170	150
ENE-Person	492	49	57
NP-Misc	948	108	96
ENE-Misc	255	31	22
Head	1,261	142	153
Domain-Mark	1,069	122	133

Download

The dataset can be downloaded from the current Github repository, Zenodo (https://zenodo.org/records/10530178) and HuggingFace https://huggingface.co/datasets/GEODE/GeoEDdA. The dataset is available in the following formats¹:

• JSONL format provided by Prodigy
• binary spaCy format (ready to use with the spaCy train pipeline)

spaCy Custom Spancat trained on Diderot & d’Alembert’s Encyclopédie entries

This dataset was used to train and evaluate a custom spancat model for French using spaCy. The model is available on HuggingFace's model hub: https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda.

How to Get Started with the model

Use the code below to get started with the model.

pip install https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda/resolve/main/fr_spacy_custom_spancat_edda-any-py3-none-any.whl

# Using spacy.load().
import spacy
nlp = spacy.load("fr_spacy_custom_spancat_edda")

# Importing as module.
import fr_spacy_custom_spancat_edda
nlp = fr_spacy_custom_spancat_edda.load()
doc = nlp("* ALBI, (Géog.) ville de France, capitale de l'Albigeois, dans le haut Languedoc : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44.")

spans = []

for span in doc.spans['sc']:
  spans.append({
      "start": span.start_char,
      "end": span.end_char,
      "labels": [span.label_],
      "text": span.text
  })

print(spans)

# Output
[{'start': 2, 'end': 6, 'labels': ['Head'], 'text': 'ALBI'},
 {'start': 16, 'end': 21, 'labels': ['NC-Spatial'], 'text': 'ville'},
 {'start': 25, 'end': 31, 'labels': ['NP-Spatial'], 'text': 'France'},
 {'start': 33, 'end': 41, 'labels': ['NC-Spatial'], 'text': 'capitale'},
 {'start': 58, 'end': 62, 'labels': ['Relation'], 'text': 'dans'},
 {'start': 92, 'end': 95, 'labels': ['Relation'], 'text': 'sur'},
 {'start': 9, 'end': 14, 'labels': ['Domain-mark'], 'text': 'Géog.'},
 {'start': 45, 'end': 56, 'labels': ['NP-Spatial'], 'text': "l'Albigeois"},
 {'start': 96, 'end': 103, 'labels': ['NP-Spatial'], 'text': 'le Tarn'},
 {'start': 16,
  'end': 31,
  'labels': ['ENE-Spatial'],
  'text': 'ville de France'},
 {'start': 63,
  'end': 80,
  'labels': ['NP-Spatial'],
  'text': 'le haut Languedoc'},
 {'start': 33,
  'end': 56,
  'labels': ['ENE-Spatial'],
  'text': "capitale de l'Albigeois"},
 {'start': 33,
  'end': 80,
  'labels': ['ENE-Spatial'],
  'text': "capitale de l'Albigeois, dans le haut Languedoc"},
 {'start': 16,
  'end': 80,
  'labels': ['ENE-Spatial'],
  'text': "ville de France, capitale de l'Albigeois, dans le haut Languedoc"}]

Evaluation

Evaluation is performed using the spacy evaluate command line interface:

• Overall model performances (Test set)

	Precision	Recall	F-score
	94.09	79.91	86.42

• Model performances by entity (Test set)

	Precision	Recall	F-score
NC-Spatial	96.50	93.24	94.84
NP-Spatial	92.74	95.95	94.32
ENE-Spatial	91.67	95.51	93.55
Relation	97.33	64.60	77.66
Latlong	0.00	0.00	0.00
NC-Person	93.07	70.68	80.34
NP-Person	92.47	90.00	91.22
ENE-Person	92.16	82.46	87.04
NP-Misc	93.24	71.88	81.18
ENE-Misc	0.00	0.00	0.00
Head	97.37	24.18	38.74
Domain-mark	99.19	91.73	95.31

Cite this work

Moncla, L., Vigier, D., & McDonough, K. (2024). GeoEDdA: A Gold Standard Dataset for Geo-semantic Annotation of Diderot & d’Alembert’s Encyclopédie. In proceedings of the 2nd International Workshop on Geographic Information Extraction from Texts (GeoExT'24), ECIR Conference, Glasgow, UK.

Moncla, L., Vigier, D., & McDonough, K. (2024). Diderot & d'Alembert's Encyclopédie: Gold Standard Corpus for Named Entity Recognition and Span Categorization Annotations (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10530178

Acknowledgement

The authors are grateful to the ASLAN project (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). Data courtesy the ARTFL Encyclopédie Project, University of Chicago.

Footnotes

1. This is a work in progress. The dataset will be available in other formats soon. ↩