Academic literature on the topic 'Traitement automatique du language'
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Journal articles on the topic "Traitement automatique du language":
Gross, Gaston, and Franz Guenthner. "Traitement automatique des domaines." Revue française de linguistique appliquée III, no. 2 (1998): 47. http://dx.doi.org/10.3917/rfla.032.0047.
Massoussi, Taoufik, and Salah Mejri. "Traitement automatique des métonymies." Revue française de linguistique appliquée XIV, no. 2 (2009): 43. http://dx.doi.org/10.3917/rfla.142.0043.
Blin, Raoul. "Traitement automatique du japonais et études linguistiques." Histoire Épistémologie Langage 23, no. 1 (2001): 33–48. http://dx.doi.org/10.3406/hel.2001.2816.
Valette, Mathieu, and Egle Eensoo. "Approche textuelle pourle traitement automatique du discours évaluatif." Langue française 184, no. 4 (2014): 109. http://dx.doi.org/10.3917/lf.184.0109.
Labelle, Jacques. "Le Traitement Automatique Des Variantes Linguistiques en Français." Lingvisticæ Investigationes. International Journal of Linguistics and Language Resources 19, no. 1 (January 1, 1995): 137–52. http://dx.doi.org/10.1075/li.19.1.09lab.
Maurel, Denis. "Grammaire des Dates Etude Préliminaire à Leur Traitement Automatique." Lingvisticæ Investigationes. International Journal of Linguistics and Language Resources 12, no. 1 (January 1, 1988): 101–28. http://dx.doi.org/10.1075/li.12.1.05mau.
Zemni, Bahia, Wafa Bedjaoui, and Marwa Elsaadany. "Recherche terminologique et traduction automatique." Texto Livre: Linguagem e Tecnologia 14, no. 1 (January 26, 2021): e26501. http://dx.doi.org/10.35699/1983-3652.2021.26501.
Paroubek, Patrick, and Joseph Mariani. "De l’évaluation et ses ressources en traitement automatique des langues." Revue française de linguistique appliquée XXIV, no. 1 (2019): 95. http://dx.doi.org/10.3917/rfla.241.0095.
Buvet, Pierre-André. "Compréhension automatique des articles politiques : le traitement des discours rapportés." Éla. Études de linguistique appliquée N° 180, no. 4 (2015): 475. http://dx.doi.org/10.3917/ela.180.0475.
Cori, Marcel. "Des méthodes de traitement automatique aux linguistiques fondées sur les corpus." Langages 171, no. 3 (2008): 95. http://dx.doi.org/10.3917/lang.171.0095.
Dissertations / Theses on the topic "Traitement automatique du language":
Colin, Émilie. "Traitement automatique des langues et génération automatique d'exercices de grammaire." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0059.
Our perspectives are educational, to create grammar exercises for French. Paraphrasing is an operation of reformulation. Our work tends to attest that sequence-to-sequence models are not simple repeaters but can learn syntax. First, by combining various models, we have shown that the representation of information in multiple forms (using formal data (RDF), coupled with text to extend or reduce it, or only text) allows us to exploit a corpus from different angles, increasing the diversity of outputs, exploiting the syntactic levers put in place. We also addressed a recurrent problem, that of data quality, and obtained paraphrases with a high syntactic adequacy (up to 98% coverage of the demand) and a very good linguistic level. We obtain up to 83.97 points of BLEU-4*, 78.41 more than our baseline average, without syntax leverage. This rate indicates a better control of the outputs, which are varied and of good quality in the absence of syntax leverage. Our idea was to be able to work from raw text : to produce a representation of its meaning. The transition to French text was also an imperative for us. Working from plain text, by automating the procedures, allowed us to create a corpus of more than 450,000 sentence/representation pairs, thanks to which we learned to generate massively correct texts (92% on qualitative validation). Anonymizing everything that is not functional contributed significantly to the quality of the results (68.31 of BLEU, i.e. +3.96 compared to the baseline, which was the generation of text from non-anonymized data). This second work can be applied the integration of a syntax lever guiding the outputs. What was our baseline at time 1 (generate without constraint) would then be combined with a constrained model. By applying an error search, this would allow the constitution of a silver base associating representations to texts. This base could then be multiplied by a reapplication of a generation under constraint, and thus achieve the applied objective of the thesis. The formal representation of information in a language-specific framework is a challenging task. This thesis offers some ideas on how to automate this operation. Moreover, we were only able to process relatively short sentences. The use of more recent neural modelswould likely improve the results. The use of appropriate output strokes would allow for extensive checks. *BLEU : quality of a text (scale from 0 (worst) to 100 (best), Papineni et al. (2002))
Saadane, Houda. "Le traitement automatique de l’arabe dialectalisé : aspects méthodologiques et algorithmiques." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAL022/document.
Millour, Alice. "Myriadisation de ressources linguistiques pour le traitement automatique de langues non standardisées." Thesis, Sorbonne université, 2020. http://www.theses.fr/2020SORUL126.
Citizen science, in particular voluntary crowdsourcing, represents a little experimented solution to produce language resources for some languages which are still little resourced despite the presence of sufficient speakers online. We present in this work the experiments we have led to enable the crowdsourcing of linguistic resources for the development of automatic part-of-speech annotation tools. We have applied the methodology to three non-standardised languages, namely Alsatian, Guadeloupean Creole and Mauritian Creole. For different historical reasons, multiple (ortho)-graphic practices coexist for these three languages. The difficulties encountered by the presence of this variation phenomenon led us to propose various crowdsourcing tasks that allow the collection of raw corpora, part-of-speech annotations, and graphic variants. The intrinsic and extrinsic analysis of these resources, used for the development of automatic annotation tools, show the interest of using crowdsourcing in a non-standardized linguistic framework: the participants are not seen in this context a uniform set of contributors whose cumulative efforts allow the completion of a particular task, but rather as a set of holders of complementary knowledge. The resources they collectively produce make possible the development of tools that embrace the variation.The platforms developed, the language resources, as well as the models of trained taggers are freely available
Kessler, Rémy. "Traitement automatique d’informations appliqué aux ressources humaines." Thesis, Avignon, 2009. http://www.theses.fr/2009AVIG0167/document.
Since the 90s, Internet is at the heart of the labor market. First mobilized on specific expertise, its use spreads as increase the number of Internet users in the population. Seeking employment through "electronic employment bursary" has become a banality and e-recruitment something current. This information explosion poses various problems in their treatment with the large amount of information difficult to manage quickly and effectively for companies. We present in this PhD thesis, the work we have developed under the E-Gen project, which aims to create tools to automate the flow of information during a recruitment process.We interested first to the problems posed by the routing of emails. The ability of a companie to manage efficiently and at lower cost this information flows becomes today a major issue for customer satisfaction. We propose the application of learning methods to perform automatic classification of emails to their routing, combining technical and probabilistic vector machines support. After, we present work that was conducted as part of the analysis and integration of a job ads via Internet. We present a solution capable of integrating a job ad from an automatic or assisted in order to broadcast it quickly. Based on a combination of classifiers systems driven by a Markov automate, the system gets very good results. Thereafter, we present several strategies based on vectorial and probabilistic models to solve the problem of profiling candidates according to a specific job offer to assist recruiters. We have evaluated a range of measures of similarity to rank candidatures by using ROC curves. Relevance feedback approach allows to surpass our previous results on this task, difficult, diverse and higly subjective
Coria, Juan Manuel. "Continual Representation Learning in Written and Spoken Language." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG025.
Although machine learning has recently witnessed major breakthroughs, today's models are mostly trained once on a target task and then deployed, rarely (if ever) revisiting their parameters.This problem affects performance after deployment, as task specifications and data may evolve with user needs and distribution shifts.To solve this, continual learning proposes to train models over time as new data becomes available.However, models trained in this way suffer from significant performance loss on previously seen examples, a phenomenon called catastrophic forgetting.Although many studies have proposed different strategies to prevent forgetting, they often rely on labeled data, which is rarely available in practice. In this thesis, we study continual learning for written and spoken language.Our main goal is to design autonomous and self-learning systems able to leverage scarce on-the-job data to adapt to the new environments they are deployed in.Contrary to recent work on learning general-purpose representations (or embeddings), we propose to leverage representations that are tailored to a downstream task.We believe the latter may be easier to interpret and exploit by unsupervised training algorithms like clustering, that are less prone to forgetting. Throughout our work, we improve our understanding of continual learning in a variety of settings, such as the adaptation of a language model to new languages for sequence labeling tasks, or even the adaptation to a live conversation in the context of speaker diarization.We show that task-specific representations allow for effective low-resource continual learning, and that a model's own predictions can be exploited for full self-learning
Grandchamp, Jean-Michel. "L'argumentation dans le traitement automatique de la langue." Paris 11, 1996. http://www.theses.fr/1996PA112016.
Mela, Augusta. "Traitement automatique de la coordination par et." Paris 13, 1992. http://www.theses.fr/1992PA132040.
Moncecchi, Guillermo. "Recognizing speculative language in research texts." Paris 10, 2013. http://www.theses.fr/2013PA100039.
This thesis presents a methodology to solve certain classification problems, particularly those involving sequential classification for Natural Language Processing tasks. It proposes the use of an iterative, error-based approach to improve classification performance, suggesting the incorporation of expert knowledge into the learning process through the use of knowledge rules. We applied and evaluated the methodology to two tasks related with the detection of hedging in scientific articles: those of hedge cue identification and hedge cue scope detection. Results are promising: for the first task, we improved baseline results by 2. 5 points in terms of F-score incorporating cue cooccurence information, while for scope detection, the incorporation of syntax information and rules for syntax scope pruning allowed us to improve classification performance from an F-score of 0. 712 to a final number of 0. 835. Compared with state-of-the-art methods, results are competitive, suggesting that the approach of improving classifiers based only on committed errors on a held out corpus could be successfully used in other, similar tasks. Additionally, this thesis proposes a class schema for representing sentence analysis in a unique structure, including the results of different linguistic analysis. This allows us to better manage the iterative process of classifier improvement, where different attribute sets for learning are used in each iteration. We also propose to store attributes in a relational model, instead of the traditional text-based structures, to facilitate learning data analysis and manipulation
Le, Kien Van. "Generation automatique de l'accord du participe passe." Paris 7, 1987. http://www.theses.fr/1987PA077257.
Nasser, Eldin Safa. "Synthèse de la parole arabe : traitement automatique de l'intonation." Bordeaux 1, 2003. http://www.theses.fr/2003BOR12745.
Books on the topic "Traitement automatique du language":
Philip, Miller, and Torris Thérèse, eds. Formalismes syntaxiques pour le traitement automatique du langage naturel. Paris: Hermes, 1990.
Biskri, Ismaïl, and Adel Jebali. Traitement automatique des langues naturelles: De l'analyse à l'application. Paris: Hermès science publications, 2011.
Namer, Fiammetta. Morphologie, lexique et traitement automatique des langues: L'analyseur DériF. Paris: Hermès science publications, 2009.
Mathieu, Yvette Yannick. Les verbes de sentiment: De l'analyse linguistique au traitement automatique. Paris: CNRS éditions, 2000.
Powers, David M. W. Machine learning of natural language. London: Springer-Verlag, 1989.
Anna, Dahl Deborah, ed. Practical spoken dialog systems. Dordrecht: Kluwer Academic Publishing, 2004.
Brigitte, Grau, and Chevallet Jean-Pierre, eds. La recherche d'informations précises: Traitement automatique de la langue, apprentissage et connaissances pour les systèmes de question-réponse. Paris: Hermès science publications, 2008.
Sabourin, Conrad. Computational text understanding: Natural language programming, argument analysis : bibliography. Montréal: Infolingua, 1994.
Goshawke, Walter. Computer translation of natural language. Wilmslow, United Kingdom: Sigma Press, 1987.
C, Garrod S., and Pickering Martin J. 1966-, eds. Language processing. Hove, East Sussex, UK: Psychology Press, 1999.
Book chapters on the topic "Traitement automatique du language":
Labelle, Jacques. "Traitement automatique et synonymie en français." In Lingvisticæ Investigationes Supplementa, 263. Amsterdam: John Benjamins Publishing Company, 1995. http://dx.doi.org/10.1075/lis.20.23lab.
Sagot, Benoît. "Construction de ressources lexicales pour le traitement automatique des langues." In Lingvisticæ Investigationes Supplementa, 217–54. Amsterdam: John Benjamins Publishing Company, 2013. http://dx.doi.org/10.1075/lis.30.07sag.
Léon, Jacqueline. "La mécanisation de dictionnaire dans les premières expériences de traduction automatique (1948-1960)." In Studies in the History of the Language Sciences, 331. Amsterdam: John Benjamins Publishing Company, 1999. http://dx.doi.org/10.1075/sihols.95.41leo.
Gicquel, Quentin, Denys Proux, Pierre Marchal, Caroline Hagége, Yasmina Berrouane, Stéfan J. Darmoni, Suzanne Pereira, Frédérique Segond, and Marie-Héléne Metzger. "Évaluation d’un outil d’aide á l’anonymisation des documents médicaux basé sur le traitement automatique du langage naturel." In Informatique et Santé, 165–76. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0285-5_15.
Colombat, Bernard. "Le traitement de la construction verbale dans la grammaire latine humaniste." In Studies in the History of the Language Sciences, 63. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/sihols.99.05col.
ŠMILAUER, Ivan. "La conjugaison tchèque à l’usage des machines et des hommes : quelques réflexions." In Des langues calculables à l'homme incalculable, 165–76. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.5273.
Larsonneur, Claire. "Alexa, Siri : la diversité linguistique au prisme des agents conversationnels." In Traduction automatique et usages sociaux des langues. Quelle conséquences pour la diversité linguistique ?, 179–97. Observatoire européen du plurilinguisme, 2021. http://dx.doi.org/10.3917/oep.beacc.2021.01.0179.
Chaumartin, François-Régis, and Pirmin Lemberger. "3. La représentation du sens." In Le traitement automatique des langues, 81–121. Dunod, 2020. http://dx.doi.org/10.3917/dunod.chaum.2020.01.0081.
Chaumartin, François-Régis, and Pirmin Lemberger. "2. Les bases de la linguistique informatique." In Le traitement automatique des langues, 63–80. Dunod, 2020. http://dx.doi.org/10.3917/dunod.chaum.2020.01.0063.
Chaumartin, François-Régis, and Pirmin Lemberger. "5. L’approche statistique." In Le traitement automatique des langues, 173–226. Dunod, 2020. http://dx.doi.org/10.3917/dunod.chaum.2020.01.0173.
Conference papers on the topic "Traitement automatique du language":
Raynal, Céline, Vanessa Andreani, Dominique Vasseur, Zakarya Chami, and Eric Hermann. "Apport du Traitement Automatique des Langues pour la catégorisation de retours d'expérience." In Congrès Lambda Mu 20 de Maîtrise des Risques et de Sûreté de Fonctionnement, 11-13 Octobre 2016, Saint Malo, France. IMdR, 2016. http://dx.doi.org/10.4267/2042/61744.
Andreani, V., E. Hermann, C. Raynal, and E. Carayon. "Apport des outils de traitement automatique des langues pour l’analyse de textes procéduraux." In Congrès Lambda Mu 19 de Maîtrise des Risques et Sûreté de Fonctionnement, Dijon, 21-23 Octobre 2014. IMdR, 2015. http://dx.doi.org/10.4267/2042/56074.
Simon, A. C., M. Avanzi, and J. P. Goldman. "La détection des proéminences syllabiques. Un aller-retour entre l'annotation manuelle et le traitement automatique." In Congrès Mondial de Linguistique Française 2008. Les Ulis, France: EDP Sciences, 2008. http://dx.doi.org/10.1051/cmlf08256.
Desnous, Florent, Anthony Larcher, and Sylvain Meignier. "Impact de la détection de la parole pour différentes tâches de traitement automatique de la parole." In XXXIIe Journées d’Études sur la Parole. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/jep.2018-63.
Bestchastnova, Eugénie Bonner. "La prise en compte des particularités psychologiques dans l’acquisition d’une langue de spécialité sur l’exemple de l’accentuation en français." In Language for International Communication. University of Latvia Press, 2023. http://dx.doi.org/10.22364/lincs.2023.14.