ANTELOPE - Une plateforme industrielle de traitement linguistique

International audienceThe Antelope linguistic platform, inspired by Meaning-Text Theory, targets the syntactic and semantic analysis of texts, and can handle large corpora. Antelope integrates several pre-existing (parsing) components as well as broad-coverage linguistic data originating from various sources. Efforts towards integration of all components nonetheless make for a homogeneous platform. Our direct contribution deals with components for semantic analysis, and the formalization of a unified text analysis model. This paper introduces the platform and compares it with state-of-the-art projects. It offers to the NLP community a feedback from a software company, by underlining the architectural measures that should be taken to ensure that such complex software remains maintainable.La plate-forme de traitement linguistique Antelope, en partie basée sur la Théorie Sens-Texte (TST), permet l'analyse syntaxique et sémantique de textes sur des corpus de volume important. Antelope intègre plusieurs composants préexistants (pour l'analyse syntaxique) ainsi que des données linguistiques à large couverture provenant de différentes sources. Un effort d'intégration permet néanmoins d'offrir une plate-forme homogène. Notre contribution directe concerne l'ajout de composants d'analyse sémantique et la formalisation d'un modèle linguistique unifié. Cet article présente la plate-forme et la compare à d'autres projets de référence. Il propose un retour d'expérience d'un éditeur de logiciel vers la communauté du TAL, en soulignant les précautions architecturales à prendre pour qu'un tel ensemble complexe reste maintenable

ANTELOPE - Une plateforme industrielle de traitement linguistique

International audienceThe Antelope linguistic platform, inspired by Meaning-Text Theory, targets the syntactic and semantic analysis of texts, and can handle large corpora. Antelope integrates several pre-existing (parsing) components as well as broad-coverage linguistic data originating from various sources. Efforts towards integration of all components nonetheless make for a homogeneous platform. Our direct contribution deals with components for semantic analysis, and the formalization of a unified text analysis model. This paper introduces the platform and compares it with state-of-the-art projects. It offers to the NLP community a feedback from a software company, by underlining the architectural measures that should be taken to ensure that such complex software remains maintainable.La plate-forme de traitement linguistique Antelope, en partie basée sur la Théorie Sens-Texte (TST), permet l'analyse syntaxique et sémantique de textes sur des corpus de volume important. Antelope intègre plusieurs composants préexistants (pour l'analyse syntaxique) ainsi que des données linguistiques à large couverture provenant de différentes sources. Un effort d'intégration permet néanmoins d'offrir une plate-forme homogène. Notre contribution directe concerne l'ajout de composants d'analyse sémantique et la formalisation d'un modèle linguistique unifié. Cet article présente la plate-forme et la compare à d'autres projets de référence. Il propose un retour d'expérience d'un éditeur de logiciel vers la communauté du TAL, en soulignant les précautions architecturales à prendre pour qu'un tel ensemble complexe reste maintenable

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance