This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation... more This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019-2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on av...
Dans ce papier, nous décrivons notre participation au défi d’analyse de texte DEFT 2018. Nous avo... more Dans ce papier, nous décrivons notre participation au défi d’analyse de texte DEFT 2018. Nous avons participé à deux tâches : (i) classification transport/non-transport et (ii) analyse de polarité globale des tweets : positifs, negatifs, neutres et mixtes. Nous avons exploité un réseau de neurone basé sur un perceptron multicouche mais utilisant une seule couche cachée.
Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet
Early risk detection can be useful in different areas, particularly those related to health and s... more Early risk detection can be useful in different areas, particularly those related to health and safety. Two tasks are proposed at CLEF eRisk-2018 for predicting mental disorder using users posts on Reddit. Depression and anorexia disorders must be detected as early as possible. In this paper, we extend the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microelectronique de Montpellier) in both tasks. The proposed model addresses this problem by modeling the temporal mood variation detected from user posts. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-the-art text vectorizations and deep language models. The proposed models perform comparably to other contributions while further experiments shows that attentive based deep language models outperformed the shallow learning text vectorizations.
This study focuses on the prediction of missing six semantic relations (such as is_a and has_part... more This study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.
Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on R... more Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia and self-harm respectively. The other one (T3) focus on estimation of the severity level of depression from a thread of user submissions. In this paper, we present the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier) in both tasks on early detection (T1 and T2). The proposed model addresses this problem by modeling the temporal mood variation detected from user posts through multistage learning phases. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-theart deep language models. The proposed models perform comparably to other contributions.
Dans le cadre d'un probleme classique de classification de sentiments, nous proposons un mode... more Dans le cadre d'un probleme classique de classification de sentiments, nous proposons un modele qui utilise 1) l'apprentissage par transfert plutot que les methodes classiques de word embedding et 2) des mecanismes d'attention permettant de se concentrer sur les parties du texte importantes pour la tâche de classification etudiee. Notre modele a ete evalue sur plusieurs jeux de donnees et montre des resultats tres competitifs. Or, si ces methodes d'apprentissage en profondeur s'averent tres efficaces, elles sont souvent considerees comme des boites noires, difficiles a interpreter. Dans cet article, nous evaluons l'impact des mecanismes d'attention traduits sous la forme de nuages de mots-cles pour aider les utilisateurs a interpreter les resultats de la classification. L'experimentation d'une telle visualisation sur plus de 85 participants a permis de montrer son interet en terme d'interpretabilite.
This paper addresses the problem of modeling textual conversations and detecting emotions. Our pr... more This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversational modeling for classifying the emotions. The approach does not rely on any hand-crafted features or lexicons. Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text. The model shows very competitive results.
La litteratie numerique occupe une place importante dans les competences necessaires aux citoyens... more La litteratie numerique occupe une place importante dans les competences necessaires aux citoyens du XXIe siecle. Cet article a pour objectif de presenter les resultats d’une enquete destinee a faire un etat des lieux de la litteratie numerique dont disposent les etudiants chinois pour l’apprentissage du francais. Les resultats montrent que ce public a acces a une diversite d’equipements informatiques et est capable de s’en servir ; mais il peine a depasser certains usages ordinaires, bien que la plupart des etudiants adopte une attitude positive vis-a-vis de l’integration des TIC dans l’apprentissage du francais.
Two tasks are proposed at CLEF eRisk-2018 on predicting mental disorder using Users posts on Redd... more Two tasks are proposed at CLEF eRisk-2018 on predicting mental disorder using Users posts on Reddit. Depression and anorexia disorders are considered to be detected as early as possible. In this paper we present the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Micro´electronique de Montpellier) in both tasks. The proposed architectures and models use only text information without any hand-crafted features or dictionaries to model the temporal mood variation detected from users posts. The proposed models use two learning phases through exploration of state-of-the-art text vectorization. The proposed models perform comparably to other contributions while experiments shows that document-level outperformed word-level vectorizations.
In this paper, we propose a new methodology for emotional speech recognition using visual deep ne... more In this paper, we propose a new methodology for emotional speech recognition using visual deep neural network models. We employ the transfer learning capabilities of the pre-trained computer vision deep models to have a mandate for the emotion recognition in speech task. In order to achieve that, we propose to use a composite set of acoustic features and a procedure to convert them into images. Besides, we present a training paradigm for these models taking into consideration the different characteristics between acoustic-based images and regular ones. In our experiments, we use the pre-trained VGG-16 model and test the overall methodology on the Berlin EMO-DB dataset for speakerindependent emotion recognition. We evaluate the proposed model on the full list of the seven emotions and the results set a new state-of-the-art.
This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation... more This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019-2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on av...
Dans ce papier, nous décrivons notre participation au défi d’analyse de texte DEFT 2018. Nous avo... more Dans ce papier, nous décrivons notre participation au défi d’analyse de texte DEFT 2018. Nous avons participé à deux tâches : (i) classification transport/non-transport et (ii) analyse de polarité globale des tweets : positifs, negatifs, neutres et mixtes. Nous avons exploité un réseau de neurone basé sur un perceptron multicouche mais utilisant une seule couche cachée.
Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet
Early risk detection can be useful in different areas, particularly those related to health and s... more Early risk detection can be useful in different areas, particularly those related to health and safety. Two tasks are proposed at CLEF eRisk-2018 for predicting mental disorder using users posts on Reddit. Depression and anorexia disorders must be detected as early as possible. In this paper, we extend the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microelectronique de Montpellier) in both tasks. The proposed model addresses this problem by modeling the temporal mood variation detected from user posts. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-the-art text vectorizations and deep language models. The proposed models perform comparably to other contributions while further experiments shows that attentive based deep language models outperformed the shallow learning text vectorizations.
This study focuses on the prediction of missing six semantic relations (such as is_a and has_part... more This study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.
Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on R... more Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia and self-harm respectively. The other one (T3) focus on estimation of the severity level of depression from a thread of user submissions. In this paper, we present the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier) in both tasks on early detection (T1 and T2). The proposed model addresses this problem by modeling the temporal mood variation detected from user posts through multistage learning phases. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-theart deep language models. The proposed models perform comparably to other contributions.
Dans le cadre d'un probleme classique de classification de sentiments, nous proposons un mode... more Dans le cadre d'un probleme classique de classification de sentiments, nous proposons un modele qui utilise 1) l'apprentissage par transfert plutot que les methodes classiques de word embedding et 2) des mecanismes d'attention permettant de se concentrer sur les parties du texte importantes pour la tâche de classification etudiee. Notre modele a ete evalue sur plusieurs jeux de donnees et montre des resultats tres competitifs. Or, si ces methodes d'apprentissage en profondeur s'averent tres efficaces, elles sont souvent considerees comme des boites noires, difficiles a interpreter. Dans cet article, nous evaluons l'impact des mecanismes d'attention traduits sous la forme de nuages de mots-cles pour aider les utilisateurs a interpreter les resultats de la classification. L'experimentation d'une telle visualisation sur plus de 85 participants a permis de montrer son interet en terme d'interpretabilite.
This paper addresses the problem of modeling textual conversations and detecting emotions. Our pr... more This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversational modeling for classifying the emotions. The approach does not rely on any hand-crafted features or lexicons. Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text. The model shows very competitive results.
La litteratie numerique occupe une place importante dans les competences necessaires aux citoyens... more La litteratie numerique occupe une place importante dans les competences necessaires aux citoyens du XXIe siecle. Cet article a pour objectif de presenter les resultats d’une enquete destinee a faire un etat des lieux de la litteratie numerique dont disposent les etudiants chinois pour l’apprentissage du francais. Les resultats montrent que ce public a acces a une diversite d’equipements informatiques et est capable de s’en servir ; mais il peine a depasser certains usages ordinaires, bien que la plupart des etudiants adopte une attitude positive vis-a-vis de l’integration des TIC dans l’apprentissage du francais.
Two tasks are proposed at CLEF eRisk-2018 on predicting mental disorder using Users posts on Redd... more Two tasks are proposed at CLEF eRisk-2018 on predicting mental disorder using Users posts on Reddit. Depression and anorexia disorders are considered to be detected as early as possible. In this paper we present the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Micro´electronique de Montpellier) in both tasks. The proposed architectures and models use only text information without any hand-crafted features or dictionaries to model the temporal mood variation detected from users posts. The proposed models use two learning phases through exploration of state-of-the-art text vectorization. The proposed models perform comparably to other contributions while experiments shows that document-level outperformed word-level vectorizations.
In this paper, we propose a new methodology for emotional speech recognition using visual deep ne... more In this paper, we propose a new methodology for emotional speech recognition using visual deep neural network models. We employ the transfer learning capabilities of the pre-trained computer vision deep models to have a mandate for the emotion recognition in speech task. In order to achieve that, we propose to use a composite set of acoustic features and a procedure to convert them into images. Besides, we present a training paradigm for these models taking into consideration the different characteristics between acoustic-based images and regular ones. In our experiments, we use the pre-trained VGG-16 model and test the overall methodology on the Berlin EMO-DB dataset for speakerindependent emotion recognition. We evaluate the proposed model on the full list of the seven emotions and the results set a new state-of-the-art.
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Papers by Waleed Ragheb