Papers by Delphine Battistelli

5 papers
Mama/Papa, Is this Text for Me? (2020.coling-main)

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Challenge: Existing methods to predict minimal age from which text can be understood for children are unresolved in computational linguistics.
Approach: They propose a method which predicts the minimum age from which a text can be understood by a recurrent neural network.
Outcome: The proposed method outperforms state-of-the-art models at sentence and text levels and achieves mean absolute errors of 1.86 and 2.28.
Exploring the Emotional Dimension of French Online Toxic Content (2024.lrec-main)

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Challenge: Emotion annotations can be used to analyze content and can be applied to content analysis.
Approach: They propose to use a corpus annotation scheme to annotate three online data sets composed of extremist, sexist and hateful messages respectively.
Outcome: The proposed method can provide new insights for content analysis and stronger empirical background for automatic content detection.
Age Recommendation for Texts (2020.lrec-1)

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Challenge: adequacy of a text’s characteristics with the person’s capacities and knowledge is critical in the case of . a child since her/his cognitive and linguistic skills are still under development.
Approach: They propose a natural language processing task which consists in predicting the age from which a text can be understood by someone.
Outcome: The proposed model outperforms psycholinguist models on a French text dataset and shows that the results are more accurate than psycholingual models.
Angry or Sad ? Emotion Annotation for Extremist Content Characterisation (2022.lrec-1)

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Challenge: Social platforms play an increasingly important role in the propagation of extremist ideas.
Approach: They propose to use a linguistic annotation scheme to characterize extremist content in French . they validate the scheme and test its ability to capture various aspects of emotions .
Outcome: The proposed method combines sociological and linguistic knowledge to characterize extremist content in French.
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)

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Challenge: Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types.
Approach: They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french.
Outcome: The proposed method clarifies the main concepts implied by the analysis of emotions as they are expressed in texts and performs a manual annotation campaign on a corpus of 1,594 texts (ca. 515K tokens) of different genres.

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