Elisabeth Murisasco


2024

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EMOLIS App and Dataset to Find Emotionally Close Cartoons
Soëlie Lerch | Patrice Bellot | Elisabeth Murisasco | Emmanuel Bruno
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We propose EMOLIS Dataset that contains annotated emotional transcripts of scenes from Walt Disney cartoons at the same time as physiological signals from spectators (breathing, ECG, eye movements). The dataset is used in EMOLIS App, our second proposal. EMOLIS App allows to display the identified emotions while a video is playing and suggest emotionally comparable videos. We propose to estimate an emotional distance between videos using multimodal neural representations (text, audio, video) that also combine physiological signals. This enables personalized results that can be used for cognitive therapies focusing on awareness of felt emotions. The dataset is designed to be suitable for all audiences and autistic people who have difficulties to recognize and express emotions.

2023

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Apprentissage de dépendances entre labels pour la classification multi-labels à l’aide de transformeurs
Haytame Fallah | Elisabeth Murisasco | Emmanuel Bruno | Patrice Bellot
Actes de CORIA-TALN 2023. Actes de l'atelier "Analyse et Recherche de Textes Scientifiques" (ARTS)@TALN 2023

Dans cet article, nous proposons des approches pour améliorer les architectures basées sur des transformeurs pour la classification de documents multi-labels. Les dépendances entre les labels sont cruciales dans ce contexte. Notre méthode, appelée DepReg, ajoute un terme de régularisation à la fonction de perte pour encourager le modèle à prédire des labels susceptibles de coexister. Nous introduisons également un nouveau jeu de données nommé “arXiv-ACM”, composé de résumés scientifiques de la bibliothèque numérique arXiv, étiquetés avec les mots-clés ACM correspondants.

2012

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An ontological approach to model and query multimodal concurrent linguistic annotations
Julien Seinturier | Elisabeth Murisasco | Emmanuel Bruno | Philippe Blache
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper focuses on the representation and querying of knowledge-based multimodal data. This work stands in the OTIM project which aims at processing multimodal annotation of a large conversational French speech corpus. Within OTIM, we aim at providing linguists with a unique framework to encode and manipulate numerous linguistic domains (from prosody to gesture). Linguists commonly use Typed Feature Structures (TFS) to provide an uniform view of multimodal annotations but such a representation cannot be used within an applicative framework. Moreover TFS expressibility is limited to hierarchical and constituency relations and does not suit to any linguistic domain that needs for example to represent temporal relations. To overcome these limits, we propose an ontological approach based on Description logics (DL) for the description of linguistic knowledge and we provide an applicative framework based on OWL DL (Ontology Web Language) and the query language SPARQL.

2010

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Multimodal Annotation of Conversational Data
Philippe Blache | Roxane Bertrand | Emmanuel Bruno | Brigitte Bigi | Robert Espesser | Gaelle Ferré | Mathilde Guardiola | Daniel Hirst | Ning Tan | Edlira Cela | Jean-Claude Martin | Stéphane Rauzy | Mary-Annick Morel | Elisabeth Murisasco | Irina Nesterenko
Proceedings of the Fourth Linguistic Annotation Workshop