Abstract
In the occasion of the Yellow Vests’ Movement, people expressed their distress and hopes in demonstrations, debates, letters, online social media or platforms, and books of grievances. This latter form of written expression interests us for its communicative and discursive characteristics. We seize the opportunity of the NooJ Conference held in Besançon to challenge ourselves and this corpus with NLP methods and tools. The present paper deals with the expression of blame in the book of grievances of Dole (Jura, France). It is a quest for the identification and collection of the utterances in which writers blame someone for doing something. To this aim, we use NooJ software to design a grammar able to describe a three-slot pattern involving the designation of (1) a demand for action; (2) the motives for the writers’ troubles; (3) the culprits. The paper describes our method that follows both inductive and deductive paths, and that relies on the DM dictionary provided with the software and on the handling of the nouns’ concordance lines.
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Notes
- 1.
This is precisely the original function of this jacket, usually employed on construction sites or in case of emergencies on roads.
- 2.
The on-site digitization of the book was made possible thanks to the portable scanner borrowed from Besançon federative research structure in human and social sciences, Maison des Sciences de l’Homme et de l’Environnement Claude Nicolas Ledoux (UAR 3124, université Bourgogne Franche-Comté/CNRS).
- 3.
One person signed three different texts under three different identities (Head of the weather station, Brother-in-law of a disabled woman, Forester); yet we could not know whether he or she would write on behalf of different persons or if he or she would intentionally display different voices. From a discourse analysis perspective, what matters is the expression of points of view; therefore, in that particular case, we have decided to count three different texts and writers.
- 4.
We have used Abbyy FineReader to try the OCR (Optical Character Reading) technique. For lack of time, we could not experiment HTR (Handwritten Text Recognition) techniques, such as the one developed by the platforms Transkribus (https://readcoop.eu/transkribus/) or e-Scriptorium (https://escripta.hypotheses.org/).
- 5.
TEI stands for Text Encoding Initiative. It is “is a consortium which collectively develops and maintains a standard for the representation of texts in digital form [and] specify encoding methods for machine-readable texts, chiefly in the humanities, social sciences and linguistics” (https://tei-c.org/).
- 6.
We thank Anaïs Rico--Perrier, our Master student in Discourse Analysis (year 2018–2019), who carried out all technical tasks with us during her 2 month training as a member of this research action: she helped with the transcription, the normalization, the anonymization and the TEI structuration of the data.
- 7.
In the table, word forms in French are ordered alphabetically; translations follow the same order as in French. Translations were produced with the help of online Larousse bilingual dictionary (https://www.larousse.fr/dictionnaires/francais-anglais/); when the exact translation could not be found, we defined the word to help understanding.
- 8.
The second version of our grammar may not be fully satisfying since it still indicates prepositions’ lexicalizations: this is caused by a technical problem we could not fix before submitting the paper.
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Bendinelli, M. (2021). Who is to Blame for What? An Insight Within the French Yellow Vests’ Movement Through Dole’s Books of Grievances. In: Bigey, M., Richeton, A., Silberztein, M., Thomas, I. (eds) Formalizing Natural Languages: Applications to Natural Language Processing and Digital Humanities. NooJ 2021. Communications in Computer and Information Science, vol 1520. Springer, Cham. https://doi.org/10.1007/978-3-030-92861-2_7
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