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In several real-world applications, extracting spatial semantics from text is critical. Spatial Role Labeling (SpRL) introduces a language-independent annotation scheme used in these applications, particularly for reasoning purposes. This paper proposes, first of all, a transfer learning method with a word embeddings-based approach for SpRL. Then, we enhance the word vectors with POS tags and CNN-based character-level representations. Finally, we propose a Residual BiLSTM CRF deep learning model to identify the spatial roles. The experimental results on two datasets: SemEval-2012 and SemEval-2013 Task 3, show that the proposed model outperforms other machine learning approaches.

International audience; The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). This estimation was based on review ratings and on sentiment polarity in the comments, with respect to specific aspects, such as “lecturer”, “presentation”, etc. Between course sessions, users stop reviewing, and also, the course contents may evolve. Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera.org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses.The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes. Among the supervised methods, the J48 decision trees algorithm yielded the best performance, compared to the naive Bayes model and the SVM model.

International audience; Les modèles de langue pré-entraînés ont prouvé leur efficacité dans la classification de texte multiclasses. Notre objectif est d'étudier et d'améliorer ce type d'approches pour la classification multilabels de texte, une tâche étonnamment peu explorée au cours de ces toutes dernières années. Cette tâche a pourtant des applications industrielles importantes telles que la recommandation de contenu, l'extraction de méta-données pour l'enrichissement des bases de données ou le routage automatique multicritères des emails. Dans cet article, notre originalité est de proposer des méthodes d'exploitation des activations des couches de sortie des transformeurs pour améliorer la performance de ces modèles pour la classification multilabels. Notre contribution concerne l'évaluation de l'utilité des méthodes de seuillage sur plusieurs modèles d'apprentissage profond, en calculant un seuil de classification global pour optimiser l'ensemble des classes (SGO), ou un seuil individuel propre à chaque classe étudiée (SI). Elle concerne aussi la proposition de deux approches pour la classification multilabels de texte. La première approche (NPA) consiste à ajouter un paramètre pour l'apprentissage du nombre de classes et/ou labels N présentes pour un exemple donné, pour considérer les classes qui correspondent aux N activations les plus élevées comme étant des labels valides. La deuxième approche (TL) consiste à ajouter une couche au transformeur pour l'apprentissage des critères utiles pour la sélection des labels pertinents. Nous évaluons ces approches sur des corpus d'articles de journaux et d'articles scientifiques. Nous avons aussi constitué et mis à disposition un jeu de données de résumés d'articles scientifiques en français que nous avons conçu à partir du dépôt d'archives ouvertes 'HAL'. Ces évaluations montrent que la performance de nos propositions dépasse celles des méthodes de l'état de l'art de classification multilabels de texte pour les jeux de données étudiés, et sont transposables à tout problème de classification multilabels utilisant les réseaux de neurones.

International audience; Les agents virtuels peuvent être utilisés pour promouvoir des comportements pro-sociaux. Dans cet article, nous explorons le potentiel des agents virtuels et de la réalité virtuelle pour lutter contre les discriminations sociales à travers l'induction d'empathie chez l'utilisateur. Nous présentons différents systèmes développés dans cette perspective et discutons les facteurs d'influence, les outils de mesures des performances de ces systèmes, ainsi que les limites actuelles de ces travaux.

The digititalization of recruitment processes did not smooth out all frictions related to this activity. Automatically searching for an ideal candidate is still hindered by one major issue: adequately modeling the needs expressed in natural language in a job offer. This problem is even more obvious when one considers the use case of recruiting expert profiles. Indeed, these profiles represent a minority of the recruitments. To assist a recruitment process concerning these profiles, an automated system requires an accurate knowledge of their activity sector. In this article we introduce the architecture of an expert profile recommender system applied to the process industry sector., La dématérialisation des processus de recrutement n'a pas fait disparaître toutes les frictions inhérentes à cette activité. La recherche automatisée d'un candidat idéal se heurte toujours à la difficulté à modéliser correctement les besoins exprimés en langage naturel dans une offre d'emploi. Le recrutement d'experts, notamment, est particulièrement difficile. En effet, ces profils concernent une proportion réduite des recrutements et leur prise en charge informatisée nécessite une connaissance précise du secteur d'activité concerné. Dans cet article, nous proposons l'architecture d'un système de recommandation de profils experts dans l'industrie des procédés afin d'assister ce type de recrutements.

Extractive multi-document summarization (MDS) is the process of automatically summarizing a collection of documents by ranking sentences according to their importance and informativeness. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Word embedding representations have shown to be effective for several Natural Language Processing (NLP) tasks including Automatic Text Summarization (ATS). However, most of these representations do not consider the order and the semantic relationships between words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose an unsupervised method for generic extractive multi-document summarization based on the sentence embedding representations and the centroid approach. The proposed method selects relevant sentences according to the final score obtained by combining three scores: sentence content relevance, sentence novelty, and sentence position scores. The sentence content relevance score is computed as the cosine similarity between the centroid embedding vector of the cluster of documents and the sentence embedding vectors. The sentence novelty metric is explicitly adopted to deal with redundancy. The sentence position metric assumes that the first sentences of a document are more relevant to the summary, and it assigns high scores to these sentences. Moreover, this paper provides a comparative analysis of nine sentence embedding models used to represent sentences as dense vectors in a low dimensional vector space in the context of extractive multi-document summarization. Experiments are performed on the standard DUC’2002–2004 benchmark datasets and the Multi-News dataset. The overall obtained results have shown that our method outperforms several state-of-the-art methods and achieves promising results compared to the best performing methods including supervised deep learning based methods.

In this paper, we introduce FreSaDa, a French Satire Data Set, which is composed of 11,570 articles from the news domain. In order to avoid reporting unreasonably high accuracy rates due to the learning of characteristics specific to publication sources, we divided our samples into training, validation and test, such that the training publication sources are distinct from the validation and test publication sources. This gives rise to a cross-domain (cross-source) satire detection task. We employ two classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (average of CamemBERT word embeddings). As an additional contribution, we present an unsupervised domain adaptation method based on regarding the pairwise similarities (given by the dot product) between the training samples and the validation samples as features. By including these domain-specific features, we attain significant improvements for both character n-grams and CamemBERT embeddings., Comment: Accepted at IJCNN 2021

International audience; We propose in this paper a statistical model in the perspective of predicting listener's feedbacks in a conversation. The first contribution of the paper is a study of the prediction of all feedbacks, including those in overlap with the speaker with a good accuracy. Existing model are good at predicting feedbacks during a pause, but reach a very low success level for all feedbacks. We give in this paper a first step towards this complex problem. The second contribution is a model predicting precisely the type of the feedback (generic vs. specific) as well as other specific features (valence expectation) useful in particular for generating feedbacks in dialogue systems. This work relies on an original corpus.

International audience; Book search is a challenging task due to discrepancies between the content and description of books, on one side, and the ways in which people query for books, on the other. However, online reviewers provide an opinionated description of the book, with alternative features that describe the emotional and experiential aspects of the book. Therefore, locating emotional sentences within reviews, could provide a rich alternative source of evidence to help improve book recommendations. Specifically, sentiment analysis (SA) could be employed to identify salient emotional terms, which could then be used for query expansion? This paper explores the employment ofSA based query expansion, in the book search domain. We introduce a sentiment-oriented method for the selection of sentences from the reviews of top rated book. From these sentences, we extract the terms to be employed in the query formulation. The sentence selection process is based on a semi-supervised SA method, which makes use of adapted word embeddings and lexicon seed-words.Using the CLEF 2016 Social Book Search (SBS) Suggestion TrackCollection, an exploratory comparison between standard pseudo-relevance feedback and the proposed sentiment-based approach is performed. The experiments show that the proposed approach obtains 24%-57% improvement over the baselines, whilst the classic technique actually degrades the performance by 14%-51%.

International audience; Les agents artificiels (robots humanoïdes et personnages virtuels), peuvent être exploités pour un monde meilleur : pour augmenter l'empathie entre les gens, améliorer leur bien-être, promouvoir les coopérations, encourager les comportements prosociaux, réduire les inégalités sociales par l'apprentissage, etc. S’intégrant dans le récent courant de recherche appelé l’Informatique Prosociale, un certain nombre de travaux dans le domaine des agents artificiels sont aujourd’hui menés avec cet objectif. Outre ces recherches dédiées, quelque que soit le domaine applicatif de nos travaux de recherche, il est impératif de veiller à la conception de nos agents artificiels. En particulier, l’apparence et le comportement de ces derniers peuvent avoir une influence inattendue et non souhaitable sur le comportement de l’utilisateur et ses croyances, non seulement au sein de l’environnement virtuel mais se poursuivant aussi ensuite dans le monde réel. Dans cet article, ces problématiques sont discutées à la lumière des résultats de recherche récents autour de l’influence des agents artificiels. De solutions sont discutés pour veiller à ce que nos agents artificiels puissent être utilisés pour tendre vers un monde meilleur.

International audience; Nous étudions l'impact de l'ajout de plusieurs modalités à la transcription de la parole pour la prédiction d'émotions. Ce travail a pour objectif de contribuer aux thérapies pour l'éducation émotionnelle des adolescents TSA (Trouble du Spectre Autistique) à partir d'une recommandation automatique de passages de séries TV.

National audience; La recherche d’information et la recommandation correspondent à des processus cognitifs complexes. Pour être efficaces, les approches algorithmiques doivent s’adapter dynamiquement et disposer d’informations nombreuses, notamment sur les contextes d’utilisation et sur les préférences des utilisateurs. C’est ainsi que se sont développées des approches personnalisées d’apprentissage automatique, exploitant de très nombreuses données de façon peu transparente. Nous présentons les approches statistiques les plus populaires, du modèle vectoriel de recherche d’information aux approches connexionnistes à bases de réseaux neuronaux. Nous soulignons les limites inhérentes aux procédures d’apprentissage dans le cadre de tâches subjectives, notamment du point de vue des risques d’uniformisation contre lesquelles une pluralité de modèles est nécessaire. Nous proposons enfin d’étudier le comportement des modèles et des systèmes automatiques sous différents angles, selon des approches pluridisciplinaires.

International audience; The emergence of social media allows users to get opinions, suggestions, or recommendations from other users about complex information needs. Tasks, as the CLEF Social Book Search 2016 Suggestion Track, propose to pursue this issue. The originality is to deal with verbose queries of book recommendation in order to support users in searching for books in catalogues of professional meta-data and complementary social media (i.e. tags, authors, similar products). In this context, a new technique for community-of-books discovery based on frequent social information (i.e. tags, authors) of similar books are proposed for book recommendation. Our method allows detecting frequent sub-graphs of similar books and using them to enrich the results returned by a traditional information retrieval system. This approach is tested on a collection containing Amazon/LibraryThing book descriptions and a set of queries, extracted from the LibraryThing discussion forums.

International audience; Search systems based on both professional meta-data (e.g., title, description, etc.) and social signals (e.g., like, comment , rating, etc.) from social networks is the trending topic in information retrieval (IR) field. This paper presents 2SRM (Social Signals Relevance Model), an approach of IR which takes into account social signals (users' actions) as an additional information to enhance a search. We hypothesize that these signals can play a role to estimate a priori social importance (relevance) of the resource (document). In this paper, we first study the impact of each such signal on retrieval performance. Next, some social properties such as popularity, reputation and freshness are quantified using several signals. The 2SRM combines the social relevance, estimated from these social signals and properties, with the conventional textual relevance. Finally, we investigate the effect of the social signals on the retrieval effectiveness using state-of-the-art learning approaches. In order to identify the most effective signals, we adopt feature selection algorithms and the correlation between the signals. We evaluated the effectiveness of our approach on both IMDb (Internet Movie Databese) and SBS (Social Book Search) datasets containing movies and books resources and their social characteristics collected from several social networks. Our experimental results are statistically significant, and reveal that incorporating social signals in retrieval model is a promising approach for improving the retrieval performance.

International audience; In this paper, we present a tool allowing dynamic prediction and visualization of an individual's local brain activity during a conversation. The prediction module of this tool is based on classifiers trained using a corpus of human-human and human-robot conversations including fMRI recordings. More precisely, the module takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant's visual input and eye movements. The visualisation module shows in real-time the dynamics of brain active areas synchronised with the behavioral raw data. In addition, it shows which integrated behavioral features are used to predict the activity in individual brain areas.

International audience; Most of the queries submitted to search engines are composed of keywords but it is not enough for users to express their needs. Through verbose natural language queries, users can express complex or highly specific information needs. However, it is difficult for search engine to deal with this type of queries. Moreover, the emergence of social medias allows users to get opinions, suggestions or recommendations from other users about complex information needs. In order to increase the understandability of user needs, tasks as the CLEF Social Book Search Suggestion Track have been proposed from 2011 to 2016. The aim is to investigate techniques to support users in searching for books in catalogs of professional metadata and complementary social media. In this context, we introduce in the current paper a statical approach to deal with long verbose queries in Social Information Retrieval (SIR) by taking Social Book Search(SBS) as a study case. firstly, a morphosyntactic analysis was introduced to reduce verbose queries, the second step is based on expanding the reduced queries using association rules mining combined with Pseudo relevance feedback. Experiments on SBS 2014 and 2016 collections show significant improvement in the retrieval performance.

International audience; This paper proposes an approach on Facebook search in Arabic and English, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic and English Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook posts. Therefore, the polarity of each comment expressed in Arabic or in English on a given Facebook post, is estimated on the basis of a neural sentiment model. Secondly , we group signals according to their complementarity using attributes (features) selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics, for each of the two languages. Experiments results reveal that Random Forests was the most effective LTR approach for this task, and for the both languages. However, the best appropriate features selection algorithms are ReliefFAttributeEval and InfoGainAttributeEval for Arabic and English Facebook search task, respectively.

International audience; This paper proposes an approach on Facebook search in Arabic, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook post. Therefore, the polarity of each comment expressed on a given Facebook post, is estimated on the basis of a neural sentiment model in Arabic language. Secondly, we group signals according to their complementarity using features selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics in Arabic language. Experiments results reveal that Random Forests combined with ReliefFAttributeEval (RLF) was the most effective LTR approach for this task.

International audience; Extractive summarization consists of generating a summary by ranking sentences from the original texts according to their importance and salience. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Distributed word vector representations have been shown to improve Natural Language Processing (NLP) tasks, especially Automatic Text Summariza-tion (ATS). However, most of them do not consider the order and the context of the words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose a deep neural network model based-method for extractive single document sum-marization using the state-of-the-art sentence embedding models. Experiments are performed on the standard DUC2002 dataset using three sentence embedding models. The obtained results show the effectiveness of the used sentence embedding models for ATS. The overall comparison results show that our method outperforms eight well-known ATS baselines and achieves comparable results to the state-of-the-art deep learning based methods.

International audience; A key challenge when studying human-agent interaction is the evaluation of user's experience. In virtual reality, this question is addressed by studying the sense of "presence" and"co-presence", generally assessed thanks to well-grounded subjective post-experience questionnaires. In this article, we aim at exploring behavioral measures of presence and co-presence by analyzing multimodal cues produced during an interaction both by the user and the virtual agent. In our study, we started from a corpus of human-agent interaction collected in a task-oriented context: a virtual environment aiming at training doctors to break bad news to a patient (played by a virtual agent). Based on this corpus, we have used machine learning algorithms to explore the possibility of predicting user's sense of presence and co-presence. In particular, we have applied and compared two techniques, Random forest and SVM, both showing very good results in predicting the level of presence and co-presence.

International audience; A key challenge when studying human-agent interaction is the evaluation of user's experience. In virtual reality, this question is addressed by studying the sense of "presence" and"co-presence", generally assessed thanks to well-grounded subjective post-experience questionnaires. In this article, we aim at exploring behavioral measures of presence and co-presence by analyzing multimodal cues produced during an interaction both by the user and the virtual agent. In our study, we started from a corpus of human-agent interaction collected in a task-oriented context: a virtual environment aiming at training doctors to break bad news to a patient (played by a virtual agent). Based on this corpus, we have used machine learning algorithms to explore the possibility of predicting user's sense of presence and co-presence. In particular, we have applied and compared two techniques, Random forest and SVM, both showing very good results in predicting the level of presence and co-presence.

International audience; Handling long queries can involve either reducing its size by retaining only useful sentences, or decomposing the long query into several short queries based on their content. A proper sentence classification improves the utility of these procedures. Can Sentiment Analysis has a role in sentence classification? This paper analysis the correlation between sentiment analysis and sentence classification in long book-search queries. Also, it studies the similarity in writing style between book reviews and sentences in book-search queries. To accomplish this study, a semi-supervised method for sentiment intensity prediction, and a language model based on book reviews are presented. In addition to graphical illustrations reflecting the feedback of this study, followed by interpretations and conclusions.

We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (human–human interaction, HHI) to an interaction with a conversational robot (human–robot interaction, HRI). The social interaction consists of 1 min blocks of live bidirectional discussion between the scanned participant and the human or robot agent. A final sample of 21 participants is included in the corpus comprising physiological (blood oxygen level-dependent, respiration and peripheral blood flow) and behavioural (recorded speech from all interlocutors, eye tracking from the scanned participant, face recording of the human and robot agents) data. Here, we present the first analysis of this corpus, contrasting neural activity between HHI and HRI. We hypothesized that independently of differences in behaviour between interactions with the human and robot agent, neural markers of mentalizing (temporoparietal junction (TPJ) and medial prefrontal cortex) and social motivation (hypothalamus and amygdala) would only be active in HHI. Results confirmed significantly increased response associated with HHI in the TPJ, hypothalamus and amygdala, but not in the medial prefrontal cortex. Future analysis of this corpus will include fine-grained characterization of verbal and non-verbal behaviours recorded during the interaction to investigate their neural correlates. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction'.

Relation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine learning algorithms. This paper tries to answer important questions concerning both the impact of semantic based features, and the integration of external linguistic knowledge resources on RE performance. For that, a RE system based on a logical and relational learning algorithm was used and evaluated on three reference datasets from two distinct domains. The yielded results confirm that the classifiers induced using the proposed richer feature set outperformed the classifiers built with morphosyntactic features in average 4% (F1-measure).

​Consumers are used to consulting posted reviews on the Internet before buying a product. But it’s difficult to know the global opinion considering the important number of those reviews. Sentiment analysis afford detecting polarity (positive, negative, neutral) in a expressed opinion and therefore classifying those reviews. Our purpose is to determine the influence of emotions on the polarity of books reviews. We define “bag-of-words” representation models of reviews which use a lexicon containing emotional (anticipation, sadness, fear, anger, joy, surprise, trust, disgust) and sentimental (positive, negative) words. This lexicon afford measuring felt emotions types by readers. The implemented supervised learning used is a Random Forest type. The application concerns Amazon platform’s reviews., Les consommateurs ont l'habitude de consulter les critiques postées sur internet avant d'acheter un produit. Mais, il est difficile pour le consommateur de connaître l'opinion globale du produit vu le nombre important de ces critiques. L'analyse des sentiments permet de détecter la polarité (positive, négative ou neutre) sur une opinion exprimée et donc de classer ces critiques. Notre but est de déterminer l'influence de l'expression des émotions sur l'analyse de la polarité des critiques de livres. Nous définissons des modèles de représentation "sac de mots" de critiques qui s'appuient sur un lexique contenant des mots porteurs de sentiments (positif, négatif) et d'émotions (anticipation, tristesse, peur, colère, joie, surprise, confiance, dégoût). Ce lexique permet de mesurer les types d'émotions ressenties par les lecteurs. L'apprentissage supervisé mis en oeuvre est de type forêt aléatoire (Random Forest). L'application concerne des critiques de la plateforme Amazon.

International audience; In human-agent interaction, one key challenge is the evaluation of the user's experience. In the virtual reality domain, the sense of presence and co-presence, reflecting the psychological immersion of the user, is generally assessed through well-grounded subjective post-experience questionnaires. In this article, we aim at presenting a new way to automatically predict the sense of presence and co-presence of a user at the end of an interaction based on specific verbal and non-verbal behavioral cues automatically computed. A random forest algorithm has been applied on a human-agent interaction corpus collected in the specific context of a virtual environment developed to train doctors to break bad news to a virtual patient. The performance of the models demonstrate the capacity to automatically and accurately predict the level of presence and co-presence, but also show the relevancy of the verbal and non-verbal behavioral cues as objective measures of presence.

Whether or not word sense disambiguation (WSD) can improve information retrieval (IR) results represents a topic that has been intensely debated over the years, with many inconclusive or contradictory conclusions. The most rarely used type of WSD for this task is the unsupervised one, although it has been proven to be beneficial at a large scale. Our study builds on existing research and tries to improve the most recent unsupervised method which is based on spectral clustering. It investigates the possible benefits of “helping” spectral clustering through feature selection when it performs sense discrimination for IR. Results obtained so far, involving large data collections, encourage us to point out the importance of feature selection even in the case of this advanced, state of the art clustering technique that is known for performing its own feature weighting. By suggesting an improvement of what we consider the most promising approach to usage of WSD in IR, and by commenting on its possible extensions, we state that WSD still holds a promise for IR and hope to stimulate continuation of this line of research, perhaps at an even more successful level.

International audience; In this paper, new schemas for feature categorization in different kinds of reviews, in the domain of books, are presented, so aspect extraction techniques could be later applied. We deal here with two types of reviews: formal reviews about scholarly books, written by experts, and informal ones about fiction books, written by readers which are not necessarily highly qualified. Our final goal is to extract the most relevant aspects or features to which any opinion is expressed in these reviews, along with the sentiment associated, for later integrating it to book recommender systems, improving the quality of the recommendations. Throughout this paper, the need for different annotation schemas is proved, by developing a new review classification system, as well as making an analysis at lexical and semantic levels on both kinds of reviews, for finally concluding with the presentation of the new categorization schemas.

International audience; The continued increase in the use of smartphones and other mobile devices has led to a substantial increase in the demand for mobile applications. With the growing availability of mobile apps, retrieving the right application from a large set has become difficult. However, the existing term-based search engines tend to retrieve relevant apps based on query terms rather than considering app features really required by users, such as functionalities, technical or user-interface characteristics. The novelty of this paper lies in extracting app features from app description and social users' reviews, extracting user-requested features and matching between them to get the feature-based score. In addition, we propose effective techniques that extract and weight features requested in the query. Finally, we combine Feature-based and term-based scores together to obtain the app Copyright 20XX Inderscience Enterprises Ltd. 2 M. Chaa et al. relevance score. The experimental results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.

International audience; Assessing the social competence of anthropomorphic artificial agents developed to produce engaging social interactions with humans has become of primary importance to effectively compare various appearances and/or behaviours. Here we attempt to objectify the social competence of artificial agents, across different dimensions, using human brain neurophysiology. Whole brain activity is recorded with functional Magnetic Resonance Imaging (fMRI) while participants, naïve to the real purpose of the experiment, discuss either with a human confederate or with an artificial agent, presently the robotic conversational head Furhat controlled with a Wizard of Oz procedure. This allows a direct comparison of local brain responses, not only at the cortical level but also in subcortical structures associated with motivational drive and impossible to investigate with non-invasive neurophysiology techniques such as surface recordings. The present data (n=21 participants) demonstrates the feasibility of this approach, and results confirm an increased activity in subcortical structures, in particular the amygdala involved in emotional processing and the hypothalamus, known to secrete, among others, the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

We will focus in this paper on how social media, in particular Twitter, were used to trigger, propagate then regulate social controversies in a way that challenged real-world knowledge about the person concerned. We have chosen as case study, the controversy surrounding the beleaguered Nobel Prize winner Sir Tim Hunt whose lifelong career was destroyed within a few days due to a social media campaign following a speech he gave on the 8 th of June 2015 during a lunch with women scientists and journalists in South Korea. This controversy illustrates challenges posed by social media for knowledge acquisition and organisation in the 21 st century. It appears that knowledge and truth will depend not so much on who you are in real life and your ability to put forward verifiable facts but on your social media network and activism in the virtual world. There is a very real possibility that the truth-value of socially-constructed KOs will be highly questionable and unverifiable with the traditional top-down approaches based on authoritative sources and will call for designing more adaptable and flexible modalities for designing KOS involving humans and machines working in real time

International audience; Query performance prediction (QPP) aims at automatically estimating the information retrieval system effectiveness for any user's query. Previous work has investigated several types of pre- and post-retrieval query performance predictors; the latter has been shown to be more effective. In this paper we investigate the use of features that were initially defined for learning to rank in the task of QPP. While these features have been shown to be useful for learning to rank documents, they have never been studied as query performance predictors. We developed more than 350 variants of them based on summary functions. Conducting experiments on four TREC standard collections, we found that Letor-based features appear to be better QPP than predictors from the literature. Moreover, we show that combining the best Letor features outperforms the state of the art query performance predictors. This is the first study that considers such an amount and variety of Letor features for QPP and that demonstrates they are appropriate for this task.

International audience; This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best pre-dictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.; Les commentaires sur des ressources Web (ex. des cours, des films) deviennent de plus en plus exploitées dans des tâches d'analyse de texte (ex. détection d'opinion, dé-tection de controverses). Cet article étudie l'intensité de contradiction dans les commentaires en exploitant diffé-rents critères tels que la variation des notations et la variation des polarités autour d'entités spécifiques (ex. aspects, sujets). Premièrement, les aspects sont identifiés en fonc-tion des distributions des termes émotionnels à proximité des noms les plus fréquents dans la collection des commen-taires. Deuxièmement, la polarité est estimée pour chaque segment de commentaire contenant un aspect. Seules les ressources ayant des commentaires contenant des aspects avec des polarités opposées sont prises en compte. Enfin, les critères sont évalués, en utilisant des algorithmes de sé-lection d'attributs, pour déterminer leur impact sur l'effi-cacité de la détection de l'intensité des contradictions. Les critères sélectionnés sont ensuite introduits dans des mo-dèles d'apprentissage pour prédire l'intensité de contradiction. L'évaluation expérimentale est menée sur une collection contenant 2244 cours et leurs 73873 commentaires, collectés à partir de coursera.org. Les résultats montrent que la variation des notations, la variation des polarités et la quantité de commentaires sont les meilleurs prédicteurs de l'intensité de contradiction. En outre, J48 est l'approche d'apprentissage la plus efficace pour cette tâche

International audience; Dans cet article, nous introduisons une méthodologie par-ticulì ere fondée sur l'analyse séquentielle de plusieurs corpus : ` a partir d'un corpus d'interaction humain-humain pour construire un système semi-autonome ayant pour objectif de recueillir un nouveau corpus d'interaction humain-machine, uné etape avant le développement d'un systèmeentì erement autonome construit sur la base de l'analyse des corpus collec-tés. La méthodologie présentée est illustrée dans le contexte du développement d'une plateforme pour la formation en réalité virtuelle des médecinsmédecins`médecinsà l'annonce d'´ evènements in-désirables graves.

International audience; Emojis are some of the most common ways to convey emotions and sentiments in social messaging applications. In order to help the user choose emojis among a vast range of possibilities, we aim at developing an automatic recommendation system based on user message analysis and real emoji usage, which goes beyond the simple dictionnary lookup that is done in the industry (mainly Android and iOS). For this purpose, we present a novel automatic emoji prediction model trained and tested on real data and based on sentiment-related features. Such a model differ from the ones learnt from tweets and can predict emojis with a 84.48% f1-score and a 95.49% high precision, using MultiLabel-RandomForest algorithm on real private instant message corpus. We want to determine the best discriminative features for this task.

International audience; In this paper we present the system submitted to the SemEval2018 task2 : Multilingual Emoji Prediction. Our system approaches both languages as being equal by first; considering word embeddings associated to automatically computed features of different types, then by applying bagging algorithm RandomForest to predict the emoji of a tweet.

International audience; Opinions (reviews) on web resources (e.g., courses, movies), generated by users, become increasingly exploited in text analysis tasks, the detection of contradictory opinions being one of them. This paper focuses on the quantification of sentiment-based contradictions around specific aspects in reviews. However, it is necessary to study the contradictions with respect to the temporal dimension of reviews (their sessions). In general, for web resources such as online courses (e.g. coursera or edX), reviews are often generated during the course sessions. Between sessions, users stop reviewing courses, and there are chances that courses will be updated. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their corresponding session. Secondly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is estimated. Then, only resources containing these aspects with opposite polarities are considered. Finally, the contradiction intensity is estimated based on the joint dispersion of polarities and ratings of the reviews containing aspects. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. The results confirm the effectiveness of our approach to find and quantify contradiction intensity.

International audience; Doctors should be trained not only to perform medical or surgical acts but also to develop competences in communication for their interaction with patients. For instance, the way doctors deliver bad news has a signifcant impact on the therapeutic process. In order to facilitate the doctors' training to break bad news, we aim at developing a virtual patient ables to interact in a multimodal way with doctors announcing an undesirable event. One of the key elements to create an engaging interaction is the feedbacks' behavior of the virtual character. In order to model the virtual patient's feedbacks in the context of breaking bad news, we have analyzed a corpus of real doctor's training. Te verbal and nonverbal signals of both the doctors and the patients have been annotated. In order to identify the types of feedbacks and the elements that may elicit a feedback, we have explored the corpus based on sequences mining methods. Rules, that have been extracted from the corpus, enable us to determine when a virtual patient should express which feedbacks when a doctor announces a bad new.

International audience; While several research works have shown that virtual agents are able to generate natural and social behaviors from users, few of them have compared these social reactions to those expressed during a human-human mediated communication. In this paper, we propose to explore the social cues expressed by a user during a mediated communication either with an embodied conversational agent or with another human. For this purpose, we have exploited a machine learning method to identify the facial and head social cues characteristics in each interaction type and to construct a model to automatically determine if the user is interacting with a virtual agent or another human. The results show that, in fact, the users do not express the same facial and head movements during a communication with a virtual agent or another user. Based on these results, we propose to use such a machine learning model to automatically measure the social capability of a virtual agent to generate a social behavior in the user comparable to a human-human interaction. The resulting model can detect automatically if the user is communicating with a virtual or real interlocutor, looking only at the user's face and head during one second.

International audience; —Depending on their application, Embodied Conversational Agents (ECAs) must be able to express various affects or social constructs such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: as Scherer puts it, they are " characteristic of an affective style that colors the entire interaction " [1]. Moreover, recent findings have demonstrated that non-verbal signals are not interpretated in isolation but along with surrounding signals: for instance, a smile followed by a gaze aversion and a head aversion may signal embarassment rather than amusement [2]. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context.

International audience; Analysis of opinions (reviews) generated by users becomes increasingly exploited by a variety of applications. It allows to follow the evolution of the opinions or to carry out investigations on products. The detection of contradictory opinions about a web resource (e.g., courses, movies, products, etc.) is an important task to evaluate the latter. This paper focuses on the problem of detecting contradictions in reviews based on the sentiment analysis around specific aspects of a resource (document). In general, for web resources such as online courses (e.g. on Coursera or edX), reviews are often generated during course sessions. Between each session users stop reviewing on the course, and this course may have updates. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their session. Secondly, certain aspects are extracted according to the distributions of the emotional terms in the vicinity of the most frequent names in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is identified. Then taking only the resources containing these aspects with opposite polarities (positive, negative). Finally, we propose a measure of contradiction intensity based on the joint dispersion of the polarity and the rating of the reviews containing the aspects within each resource. The evaluation of our approach is conducted on the Massive Open Online Courses (MOOC) collection containing 2244 courses and their 73,873 reviews, collected from Coursera. The results of experiments revealed the effectiveness of the proposed approach to capture and quantify contradiction intensity.

International audience; A large amount of social feedback expressed by social signals (e.g. like, +1, rating) are assigned to web resources. These signals are often exploited as additional sources of evidence in search engines. Our objective in this paper is to study the impact of the new social signals, called Facebook reactions (love, haha, angry, wow, sad) in the retrieval. These reactions allow users to express more nuanced emotions compared to classic signals (e.g. like, share). First, we analyze these reactions and show how users use these signals to interact with posts. Second, we evaluate the impact of each such reaction in the retrieval, by comparing them to both the textual model without social features and the first classical signal (like-based model). These social features are modeled as document prior and are integrated into a language model. We conducted a series of experiments on IMDb dataset. Our findings reveal that incorporating social features is a promising approach for improving the retrieval ranking performance.

International audience; In this paper, we extensively study the impact of social signals (users’ actions) obtained from several social networks on search ranking task. Social signals associated with web resources (documents) can be considered as an additional information that can play a vital role to estimate a priori importance of these resources.Particularly, we are interested in the freshness of signals and their diversity. We hypothesize that the moment (the date) when the user actions occur and the diversity of actions may impact the search performance. We propose to model these heterogeneous social features as document prior. We evaluate the effectiveness of our approach by carrying out extensive experiments on two different INEX datasets, namely SBS and IMDb, enriched with several social signals collected from social networks. Our experimental results consistently demonstrate the interest of integrating fresh and diverse signals in the retrieval process.

National audience; Analysis of opinions (reviews) generated by users becomes increasingly exploited by a variety of applications. It allows to follow the evolution of the opinions or to carry out investigations on products. The detection of contradictory opinions about a Web resource (e.g., courses, movies, products, etc.) is an important task to evaluate the latter. In this paper, we focus on the problem of detecting contradictions based on the sentiment analysis around specific aspects of a resource (document). First, we identify certain aspects, according to the distributions of the emotional terms in the vicinity of the most frequent names in the whole of the reviews. Second, we estimate the polarity of each review segment containing one aspect. Then we take only the resources containing these aspects with opposite polarities (positive, negative). Third, we introduce a measure of the intensity of the contradiction based on the joint dispersion of the polarity and the rating of the reviews containing the aspects within each resource. We evaluate the effectiveness of our approach on the Massive Open Online Courses (MOOC) collection containing 2244 courses and their 73873 reviews, collected from Coursera. Our results show the effectiveness of the proposed approach to capture contradictions significantly.; L'analyse des avis (commentaires) générés par les utilisateurs devient de plus en plus exploitable par une variété d'applications. Elle permet de suivre l'évolution des avis ou d'effectuer des enquêtes sur des produits. La détection d’avis contradictoires autour d’une ressource Web (ex. cours, film, produit, etc.) est une tâche importante pour évaluer cette dernière. Dans cet article, nous nous concentrons sur le problème de détection des contradictions et de la mesure de leur intensité en se basant sur l’analyse du sentiment autour des aspects spécifiques à une ressource (document). Premièrement, nous identifions certains aspects, selon les distributions des termes émotionnels au voisinage des noms les plus fréquents dans l’ensemble des commentaires. Deuxièmement, nous estimons la polarité de chaque segment de commentaire contenant un aspect. Ensuite, nous prenons uniquement les ressources contenant ces aspects avec des polarités opposées (positive, négative). Troisièmement, nous introduisons une mesure de l'intensité de la contradiction basée sur la dispersion conjointe de la polarité et du rating des commentaires contenant les aspects au sein de chaque ressource. Nous évaluons l’efficacité de notre approche sur une collection de MOOC (Massive Open Online Courses) contenant 2244 cours et leurs 73873 commentaires, collectés à partir de Coursera. Nos résultats montrent l'efficacité de l’approche proposée pour capturer les contradictions de manière significative.

A large amount of social feedback expressed by social signals (e.g. like, +1, rating) are assigned to web resources. These signals are often exploited as additional sources of evidence in search engines. Our objective in this paper is to study the impact of the new social signals, called Facebook reactions (love, haha, angry, wow, sad) in the retrieval. These reactions allow users to express more nuanced emotions compared to classic signals (e.g. like, share). First, we analyze these reactions and show how users use these signals to interact with resources (e.g. posts, video, etc). Second, we evaluate the impact of each such reaction in the retrieval, by comparing them to both the textual model without social features and the first classical signal (like-based model). These social features are modeled as document prior and are integrated into a language model. We conducted a series of experiments on INEX IMDb dataset. Our findings reveal that incorporating social features is a promising approach for improving the retrieval ranking performance., Une grande partie des traces des utilisateurs exprimées par des signaux sociaux (ex. j’aime, +1, rating) sont attribuées aux ressources web. Ces signaux sont souvent exploités par les systèmes de RI comme des sources d’évidence additionnelles pour trier les résultats de recherche. Notre objectif dans cet article est d’étudier l’impact des nouveaux signaux sociaux, appelés Facebook reactions (j’adore, haha, grrr, wouah, triste) sur le tri de ces résultats. Ces réactions permettent aux utilisateurs d’exprimer des émotions plus nuancés par rapport aux signaux classiques (ex. like, share). Nous analysons tout d’abord ces réactions et montrons la manière dont les internautes les utilisent pour interagir avec les ressources (ex. posts, vidéo, etc). Ensuite, nous évaluons l’impact de ces réactions dans le tri des résultats de recherche en les comparant à un modèles de tri textuel et à un modèle qui prend en compte le signal j’aime. Ces caractéristiques sociales sont modélisées comme une probabilité a priori du document et elles sont intégrées dans un modèle de langue. Nous avons effectué une série d’expérimentations sur la collection INEX IMDb. Nos résultats révèlent que la prise en compte de ces signaux améliore significativement la qualité des résultats de recherche

International audience; In this paper we propose an approach that exploits social data associated with a Web resource to measure its a priori relevance. We show how these interaction traces left by the users on the resources, which are in the form of social signals as the number of like and share, can be exploited to quantify social properties such as popularity and reputation. We propose to model these properties as a priori probability that we integrate into language model. We evaluated the effectiveness of our approach on IMDb dataset containing 167438 resources and their social signals collected from several social networks. Our experimental results are statistically significant and show the interest of integrating social properties in a search model to enhance the information retrieval.

Serious Games (SG) are more and more used for training, as in the crisis management domain, where several hundred stakeholders can be involved, causing various organizational difficulties on field exercises. SGs specific benefits include player immersion and detailed players’ actions tracking during a virtual exercise. Moreover, Non Player Characters (NPC) can adapt the crisis management exercise perimeter to the available stakeholders or to specific training objectives. In this paper we present a Multi-Agent System architecture supporting behavioural simulation as well as monitoring and assessment of human players. A NPC is enacted by a Game Agent which reproduces the behaviour of a human actor, based on a deliberative model (Belief Desire Intention). To facilitate the scenario design, an Agent editor allows a designer to configure agents’behaviours. The behaviour simulation was implemented within the pre-existing SIMFOR project, a serious game for training in crisis management.

International audience; The INEX QA track aimed to evaluate complex question-answering tasks where answers are short texts generated from the Wiki-pedia by extraction of relevant short passages and aggregation into a coherent summary. In such a task, Question-answering, XML/passage retrieval and automatic summarization are combined in order to get closer to real information needs. Based on the groundwork carried out in 2009-2010 edition to determine the sub-tasks and a novel evaluation methodology, the 2011 edition experimented contextualizing tweets using a recent cleaned dump of the Wikipedia. Participants had to contextu-alize 132 tweets from the New York Times (NYT). Informativeness of answers has been evaluated, as well as their readability. 13 teams from 6 countries actively participated to this track. This tweet contextualiza-tion task will continue in 2012 as part of the CLEF INEX lab with same methodology and baseline but on a much wider range of tweet types.

International audience; In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the co-occurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.

International audience; In this paper we present investigation of real-life, bi-directional conversations. We introduce the multimodal corpus derived from these natural conversations alternating between human-human and human-robot interactions. The human-robot interactions were used as a control condition for the social nature of the human-human conversations. The experimental set up consisted of conversations between the participant in a functional magnetic resonance imaging (fMRI) scanner and a human confederate or conversational robot outside the scanner room, connected via bidirectional audio and unidirectional videoconferencing (from the outside to inside the scanner). A cover story provided a framework for natural, real-life conversations about images of an advertisement campaign. During the conversations we collected a multimodal corpus for a comprehensive characterization of bi-directional conversations. In this paper we introduce this multimodal corpus which includes neural data from functional magnetic resonance imaging (fMRI), physiological data (blood flow pulse and respiration), transcribed conversational data, as well as face and eye-tracking recordings. Thus, we present a unique corpus to study human conversations including neural, physiological and behavioral data.