# A dataset for automatic detection of places in (early) modern French texts

Simon Gabay, Pedro Javier Ortiz Suárez

<figure><img src="image_1.png" /></figure>

## To cite this version:

Simon Gabay, Pedro Javier Ortiz Suárez. A dataset for automatic detection of places in (early) modern French texts. NASSCFL 2021 - 50th Annual North American Society for Seventeenth-Century French Literature Conference, NASSCFL, May 2021, Iowa City / Virtual, United States. pp.5. hal-03187097

HAL Id: hal-03187097

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Submitted on 31 Mar 2021

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
A dataset for automatic detection of places in
(early) modern French texts

Simon Gabay¹ and Pedro Ortiz Suárez²

¹University of Geneva (Switzerland)

²INRIA/Sorbonne Université (France)

With the apparition of distant reading (Moretti 2013), graphs, trees but also maps (cf. fig. 1) are becoming slowly a new way to "read" (Moretti 2005). But if it is easy to manually list places in a text, it is obviously too complicated to do the same on huge corpora. In order to accelerate the extraction of the information and simplify the production of such visualisations, it is therefore crucial to detect computationally place names.

<figure><img src="image_3.png" /><figcaption>Figure 1: Place names mentioned in 17th c. place names (Gabay and Vitali 2019)</figcaption></figure>

Sophisticated tools exist for contemporary French (Ortiz Suárez et al. 2020), but their results are heavily affected by spelling variation in historical documents, for which no tool exist (Gabay and Vitali 2019). Indeed, no matter how close they are, for a computer, *geneve* is not the same as *Genève* (Kudo and Richardson 2018) (cf. exp. 1). A short-term answer to the problem is the alignment of old forms with contemporary ones (Kogkitsidou and Gambette 2020), but normalisation techniques are far from being reliable enough despite recent improvements in the field (Gabay and Barrault 2020).
## Example 1: Example of linguistic normalisation

Following our previous work at the crossroads of natural language processing and 17th c. French literature (Gabay, Bartz, and Deguin 2020; Gabay, Camps, and Clérice 2020), we have decided to create a dataset for (early) modern French (16th-18th c.) and make it available to specialists of machine learning to train new efficient models for Named Entity Recognition and Classification (NERC), but also Named Entity Linking (EL) (Frontini, Brando, and Ganascia 2015). To do so, a completely revised version of the *Presto* gold corpus (Gabay, Clérice, et al. 2020) of c. 4,900,000 lines is currently being annotated (cf. Appendix) using fine-grained entity types (following the Quaero guidelines, cf. Rosset, Grouin, and Zweigenbaum 2011) augmented, when possible, with wikidata IDs (Müller-Birn et al. 2015). This should enable researcher not only to locate place names in texts, but also to retrieve geographic coordinates to easily create maps.

Being orders of magnitude bigger than previously annotated corpora in historical French (Ehrmann et al. 2020), contemporary French (Sagot, Richard, and Stern 2012) and even English (Pradhan et al. 2012; Tjong Kim Sang and De Meulder 2003); this revised version of the *Presto* gold corpus could prove to be an useful tool for researchers in NLP (Natural Language Processing) given already very promising results even when used to train simplistic NER models (Lample et al. 2016).

Finally, if training NERC and EL models is a task for computer scientists, the creation of corpora and their annotation should be done in collaboration by specialists of the humanities. Preparing datasets being extremely time consuming, it is however important to avoid competing practices among researchers and projects, and therefore to engage the debate at the broader level to determine common annotation principles.

## Data

Data are available at the following address: github.com/e-ditiones/LEM17.

## Acknowledgments

Un grand merci à Giovanni Pietro Vitali (UVSQ) pour ses conseils en cartographie numérique.
Appendix

<table>
  <tr>
    <td>Grégoire</td>
    <td>Grégoire</td>
    <td>Np</td>
    <td>B-pers</td>
    <td>B-pers.ind</td>
    <td>B-comp.name</td>
    <td>O</td>
    <td>Q133063</td>
  </tr>
  <tr>
    <td>VII</td>
    <td>7</td>
    <td>Mc</td>
    <td>I-pers</td>
    <td>I-pers.ind</td>
    <td>B-comp.qualifier</td>
    <td>O</td>
    <td>Q133063</td>
  </tr>
  <tr>
    <td>était</td>
    <td>être</td>
    <td>Vuc</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>avec</td>
    <td>avec</td>
    <td>S</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>la</td>
    <td>le</td>
    <td>Da</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>comtesse</td>
    <td>comte</td>
    <td>Nc</td>
    <td>B-pers</td>
    <td>B-pers.ind</td>
    <td>B-comp.title</td>
    <td>O</td>
    <td>Q464162</td>
  </tr>
  <tr>
    <td>Matilde</td>
    <td>Matilde</td>
    <td>Np</td>
    <td>I-pers</td>
    <td>I-pers.ind</td>
    <td>B-comp.name</td>
    <td>O</td>
    <td>Q464162</td>
  </tr>
  <tr>
    <td>dans</td>
    <td>dans</td>
    <td>S</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>la</td>
    <td>le</td>
    <td>Da</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>ville</td>
    <td>ville</td>
    <td>Nc</td>
    <td>B-loc</td>
    <td>B-loc.adm.town</td>
    <td>B-comp.kind</td>
    <td>O</td>
    <td>Q111144</td>
  </tr>
  <tr>
    <td>de</td>
    <td>de</td>
    <td>S</td>
    <td>I-loc</td>
    <td>I-loc.adm.town</td>
    <td>O</td>
    <td>O</td>
    <td>Q111144</td>
  </tr>
  <tr>
    <td>Canosse</td>
    <td>Canossa</td>
    <td>Np</td>
    <td>I-loc</td>
    <td>I-loc.adm.town</td>
    <td>B-comp.name</td>
    <td>O</td>
    <td>Q111144</td>
  </tr>
  <tr>
    <td>,</td>
    <td>,</td>
    <td>Fw</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>l'</td>
    <td>le</td>
    <td>Da</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>ancien</td>
    <td>ancien</td>
    <td>Ag</td>
    <td>B-loc</td>
    <td>B-loc.adm.town</td>
    <td>B-comp-qualifier</td>
    <td>O</td>
    <td>Q111144</td>
  </tr>
  <tr>
    <td>Canusium</td>
    <td>Canusium</td>
    <td>Np</td>
    <td>I-loc</td>
    <td>I-loc.adm.town</td>
    <td>B-comp.name</td>
    <td>O</td>
    <td>Q111144</td>
  </tr>
  <tr>
    <td>,</td>
    <td>,</td>
    <td>Fw</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>sur</td>
    <td>sur</td>
    <td>S</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>l'</td>
    <td>le</td>
    <td>Da</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Apennin</td>
    <td>Apennin</td>
    <td>Np</td>
    <td>B-loc</td>
    <td>B-loc.geo.phys</td>
    <td>O</td>
    <td>O</td>
    <td>Q1285</td>
  </tr>
  <tr>
    <td>près</td>
    <td>près</td>
    <td>Rg</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>de</td>
    <td>de</td>
    <td>S</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Régio</td>
    <td>Reggio</td>
    <td>Np</td>
    <td>B-loc</td>
    <td>B-loc.adm.town</td>
    <td>O</td>
    <td>O</td>
    <td>Q13360</td>
  </tr>
  <tr>
    <td>,</td>
    <td>,</td>
    <td>Fw</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>forteresse</td>
    <td>forteresse</td>
    <td>Nc</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>qui</td>
    <td>qui</td>
    <td>Pr</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>passait</td>
    <td>passer</td>
    <td>Vvc</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>alors</td>
    <td>alors</td>
    <td>Rg</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>pour</td>
    <td>pour</td>
    <td>S</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>imprenable</td>
    <td>imprenable</td>
    <td>Ag</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>.</td>
    <td>.</td>
    <td>Fs</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
</table>

<table>
  <tr>
    <td>Les</td>
    <td>le</td>
    <td>Da</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>allemands</td>
    <td>allemand</td>
    <td>Nc</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>élurent</td>
    <td>élire</td>
    <td>Vvc</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>pour</td>
    <td>pour</td>
    <td>S</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>O</td>
    <td>-</td>
  </tr>
  <tr>
    <td>empereur</td>
    <td>empereur</td>
    <td>Nc</td>
    <td>B-pers</td>
    <td>B-pers.ind</td>
    <td>B-comp.title</td>
    <td>O</td>
    <td>Q438435</td>
  </tr>
  <tr>
    <td>Rodolphe</td>
    <td>Rodolphe</td>
    <td>Np</td>
    <td>I-pers</td>
    <td>I-pers.ind</td>
    <td>B-comp.name</td>
    <td>O</td>
    <td>Q438435</td>
  </tr>
  <tr>
    <td>duc</td>
    <td>duc</td>
    <td>Nc</td>
    <td>I-pers</td>
    <td>I-pers.ind</td>
    <td>B-comp.title</td>
    <td>O</td>
    <td>Q438435</td>
  </tr>
  <tr>
    <td>de</td>
    <td>de</td>
    <td>S</td>
    <td>I-pers</td>
    <td>I-pers.ind</td>
    <td>I-comp.title</td>
    <td>O</td>
    <td>Q438435</td>
  </tr>
  <tr>
    <td>Suabe</td>
    <td>Souabe</td>
    <td>Np</td>
    <td>I-pers</td>
    <td>I-pers.ind</td>
    <td>I-comp.title</td>
    <td>B-loc.adm.reg</td>
    <td>Q438435</td>
  </tr>
</table>

Example 2: NERC Fine-Grained annotation with EL
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