Éditeur(s) : HAL CCSD Presses Universitaires de Louvain (distribution I6DOC/CIACO)
Résumé : In French. 10 pages, 5 figures, LaTeX 2e using EPSF and custom package taln2006.sty (designed by Pierre Zweigenbaum, ATALA). Proceedings of the 13th annual French-speaking conference on Natural Language Processing: `Traitement Automatique des Langues Naturelles' (TALN 2006), Louvain (Leuven), Belgium, 10-13 April 2003
National audience
We propose a theoretical framework within which information on the vocabulary of a given corpus can be inferred on the basis of statistical information gathered on that corpus. Inferences can be made on the categories of the words in the vocabulary, and on their syntactical properties within particular languages. Based on the same statistical data, it is possible to build matrices of syntagmatic similarity (bigram transition matrices) or paradigmatic similarity (probability for any pair of words to share common contexts). When clustered with respect to their syntagmatic similarity, words tend to group into sublanguage vocabularies, and when clustered with respect to their paradigmatic similarity, into syntactic or semantic classes. Experiments have explored the first of these two possibilities. Their results are interpreted in the frame of a Markov chain modelling of the corpus' generative processe(s): we show that the results of a spectral analysis of the transition matrix can be interpreted as probability distributions of words within clusters. This method yields a soft clustering of the vocabulary into sublanguages which contribute to the generation of heterogeneous corpora. As an application, we show how multilingual texts can be visually segmented into linguistically homogeneous segments. Our method is specifically useful in the case of related languages which happened to be mixed in corpora.
Verbum ex machina : Actes de la 13ème conférence annuelle sur le Traitement Automatique des Langues Naturelles
Louvain (Leuven), Belgium

hal-00327607
https://hal.archives-ouvertes.fr/hal-00327607
ARXIV : 0810.1212

Éditeur(s) : HAL CCSD
Résumé : International audience
“Affective Computing is computing that relates to, arises from, or deliberately influences emotions” (R.W. Picard, MIT Press, 1997). Specifically, psychosocial equilibrium is now considered as a major issue for both individual and public health. Predicting human emotions via non-intrusive methods is a great challenge triggered by the rise of intelligent assisting systems. In order to determine the emotional state of a subject having a potential psychosocial disorder, it is required to process both neutral and expressive emotions. A computer-engineered method aiming to provide emotional information from videos is proposed. An original approach for affect classification and its interpretation is presented.The database is part of the HUMAINE database, and consists of a large volume of video clips, each displaying individuals, depressed or not, talking with a conversational agent. Both the high-dimensionality of data and its temporal aspect require a pre-training that consists of cleaning, sorting and feature selection, in order to make relevant information usable for further processing by MachineLearning methods. Next step consists in classifying the affective state of subjects according to their positivity or negativity (Russell's model, 1980). By means of six independent classifiers, each running a Support Vector Machine algorithm, separate performances are obtained for each class of emotions. The results reveal an interesting “neutral area phenomenon”, where emotions tend to be harder to be detected.Though more frequent, neutral emotions seem to carry less discriminant information than expressive ones. This issue will be a key for designing a tuning step in further research.
Caribbean Academy Of Science
Deshaies, France

hal-01408029
https://hal.univ-antilles.fr/hal-01408029

Éditeur(s) : HAL CCSD ATALA (Association pour le Traitement Automatique des Langues)
Résumé : In French. 10 pages, 4 figures, LaTeX 2e using EPSF and custom package Taln2003.sty (JC/PZ, ATALA). Proceedings of the 10th annual French-speaking conference on Natural Language Processing: `Traitement Automatique des Langues Naturelles' (TALN 2003), Batz-sur-mer, France, 10-14 June 2003
National audience
In this article, some first elements of a computational modelling of the grammar of the Martiniquese French Creole dialect are presented. The sources of inspiration for the modelling is the functional description given by Damoiseau (1984), and Pinalie's & Bernabe's (1999) grammar manual. Based on earlier works in text generation (Vaillant, 1997), a unification grammar formalism, namely Tree Adjoining Grammars (TAG), and a modelling of lexical functional categories based on syntactic and semantic properties, are used to implement a grammar of Martiniquese Creole which is used in a prototype of text generation system. One of the main applications of the system could be its use as a tool software supporting the task of learning Creole as a second language.
Nous présenterons dans cette communication les premiers travaux de modélisation informatique d'une grammaire de la langue créole martiniquaise, en nous inspirant des descriptions fonctionnelles de Damoiseau (1984) ainsi que du manuel de Pinalie & Bernabé (1999). Prenant appui sur des travaux antérieurs en génération de texte (Vaillant, 1997), nous utilisons un formalisme de grammaires d'unification, les grammaires d'adjonction d'arbres (TAG d'après l'acronyme anglais), ainsi qu'une modélisation de catégories lexicales fonctionnelles à base syntaxico-sémantique, pour mettre en oeuvre une grammaire du créole martiniquais utilisable dans une maquette de système de génération automatique. L'un des intérêts principaux de ce système pourrait être son utilisation comme logiciel outil pour l'aide à l'apprentissage du créole en tant que langue seconde.
TALN 2003 : Actes de la 10ème conférence annuelle sur le Traitement Automatique des Langues Naturelles
Batz-sur-mer, France

hal-00327553
https://hal.archives-ouvertes.fr/hal-00327553
ARXIV : 0810.1199