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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6 7 8 9 10...48 Hits 101 – 120 of 943 101 Location extraction from tweets Hoang, Thi Bich Ngoc; Mothe, Josiane In: ISSN: 0306-4573 ; Information Processing and Management ; https://hal.archives-ouvertes.fr/hal-02640811 ; Information Processing and Management, Elsevier, 2018, 54 (2), pp.129-144. ⟨10.1016/j.ipm.2017.11.001⟩ (2018) BASE Show details 102 CROWD-BASED TECHNIQUES TO IMPROVE INTELLIGENCE ANALYSIS Srinivasan, Sridhar. - : Monterey, CA; Naval Postgraduate School, 2018 BASE Show details 103 Attribution and Artificial Intelligence: Embedding Provenance with Material Metadata Doyle, Shelby; Senske, Nicholas In: Architecture Publications (2018) BASE Show details 104 Special issue on Sensing, Data Analysis and Platforms for Ubiquitous Intelligence Chen, Liming; Chen, G.; Yu, H.. - : MDPI, 2018 BASE Show details 105 The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us? André Kretzschmar; Marion Spengler; Anna-Lena Schubert... In: Journal of Intelligence ; Volume 6 ; Issue 3 (2018) BASE Show details 106 The Great Debate: General Ability and Specific Abilities in the Prediction of Important Outcomes Harrison J. Kell; Jonas W. B. Lang In: Journal of Intelligence ; Volume 6 ; Issue 3 (2018) BASE Show details 107 The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us? Kretzschmar, André; Spengler, Marion; Schubert, Anna-Lena. - : Humboldt-Universität zu Berlin, 2018 BASE Show details 108 Semantic reranking of CRF label sequences for verbal multiword expression identification ; Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop Maldonado Guerra, Alfredo; Moreau, Erwan; Vogel, Carl. - : Language Science Press, 2018 BASE Show details 109 Precision immunoprofiling by image analysis and artificial intelligence Koelzer, VH; Sirinukunwattana, K; Rittscher, J. - 2018 BASE Show details 110 Enhancing Your Intelligence Agency Information Resource IQ: PT. 1: The Office of the Director of National Intelligence Chapman, Bert In: Libraries Faculty and Staff Presentations (2018) BASE Show details 111 Troping the Enemy: Metaphor, Culture, and the Big Data Black Boxes of National Security Albro, Robert In: Secrecy and Society (2018) BASE Show details 112 The relation of personality and intelligence - what can the Brunswik symmetry principle tell us? Kretzschmar, André; Spengler, Marion; Schubert, Anna Lena. - 2018 BASE Show details 113 A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter Alqarafi, Abdulrahman; Adeel, Ahsan; Hawalah, Ahmed. - : Springer International Publishing, 2018. : Cham, Switzerland, 2018 BASE Show details 114 Pensamiento crítico de los jóvenes ciudadanos frente a las noticias en Chile Vernier, Matthieu; Cárcamo Montero, Luis; Scheiling García, Eliana In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 54, 2018, pags. 101-110 (2018) BASE Show details 115 Text mining : a guidebook for the social sciences Ignatow, Gabe; Mihalcea, Rada F.. - Los Angeles : SAGE, 2017 BLLDB UB Frankfurt Linguistik Show details 116 The future of translation technology : towards a world without Babel Chan, Sin-wai. - New York : Routledge, 2017 BLLDB UB Frankfurt Linguistik Show details 117 ImproteK: introducing scenarios into human-computer music improvisation Nika, Jérôme; Chemillier, Marc; Assayag, Gérard In: ACM Computers in Entertainment ; https://hal.archives-ouvertes.fr/hal-01380163 ; ACM Computers in Entertainment, 2017, ⟨10.1145/3022635⟩ (2017) BASE Show details 118 Facial Expression Emotion Detection for Real-time Embedded Systems Meng, H; Swash, M; Pleva, M. - 2017 BASE Show details 119 FrenchSentiClass : an Automated System for French Sentiment Classification ; FrenchSentiClass : un Système Automatisé pour la Classification de Sentiments en Français Tapi Nzali, Mike Donald; Abdaoui, Amine; Azé, Jérôme; Bringay, Sandra; Lavergne, Christian; Mollevi, Caroline; Poncelet, Pascal In: Actes de l’atelier DEFT de la conférence TALN 2017 ; DEFT: Défi Fouille de Texte ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411 ; DEFT: Défi Fouille de Texte, Jun 2017, Orléans, France (2017) Abstract: National audience ; This paper describes the system we used on the tasks of the text mining challenge (DEFT 2017). This thirteenth edition of this challenge concerned the analysis of opinions and figurative language in French tweets. Three tasks have been proposed : (i) the first one concerns the classification of non-figurative tweets according to their polarity ; (ii) the second one concerns the identification of figurative language, while (iii) the third one concerns the classification of figurative and non-figurative tweets according to their polarity. We proposed an automated system based on Support Vector Machines (SVM). The system automatically chooses on each step the best preprocessing, syntactic features and sentiment lexicons by cross validation on the training set. Furthermore, it performs an evaluation of feature subset selection and a tuning SVM complexity parameter. Therefore, this system can significantly reduce the time necessary to explore the data and choose the best feature representation. ; Ce papier décrit le système FrenchSentiClass que nous avons utilisé pour les tâches du défi de fouilles de texte (DEFT 2017). Cette treizième édition du défi a porté sur l'analyse de l'opinion et du langage figuratif dans des tweets rédigés en Français. Le défi propose trois tâches : (i) la première concerne la classification des tweets non figuratifs selon leur polarité ; (ii) la deuxième concerne l'identification du langage figuratif et (iii) la troisième concerne la classification des tweets figuratifs et non figuratifs selon leur polarité. Nous avons proposé un système automatisé basé sur les Machines à Vecteurs de Support (SVM). Le système choisit automatiquement à chaque niveau les meilleurs prétraitements, descripteurs syntaxiques et lexiques de sentiments en validation croisée sur l'ensemble d'apprentissage. Il effectue aussi une évaluation de l'apport de la sélection d'attributs et un tuning du paramètre de complexité du modèle SVM. Par conséquent, ce système permet de réduire considérablement le temps d'exploration des données et du choix de la meilleur représentation de descripteurs. ABSTRACT FrenchSentiClass : an Automated System for French Sentiment Classification This paper describes the system we used on the tasks of the text mining challenge (DEFT 2017). This thirteenth edition of this challenge concerned the analysis of opinions and figurative language in French tweets. Three tasks have been proposed : (i) the first one concerns the classification of non-figurative tweets according to their polarity ; (ii) the second one concerns the identification of figurative language, while (iii) the third one concerns the classification of figurative and non-figurative tweets according to their polarity. We proposed an automated system based on Support Vector Machines (SVM). The system automatically chooses on each step the best preprocessing, syntactic features and sentiment lexicons by cross validation on the training set. Furthermore, it performs an evaluation of feature subset selection and a tuning SVM complexity parameter. Therefore, this system can significantly reduce the time necessary to explore the data and choose the best feature representation. MOTS-CLÉS : Analyse d'opinions, détection de polarité, langage figuratif. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Analyse d’opinions; Détection de polarité; Figurative language; Langage figuratif; Opinion analysis; Polarity detection URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411 https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411/file/deft2017-frenchsenticlassEquipe4.pdf https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411/document BASE Hide details 120 La cartographie des traces textuelles comme méthodologie d’enquête en sciences sociales Cointet, Jean-Philippe. - : HAL CCSD, 2017 In: https://hal-sciencespo.archives-ouvertes.fr/tel-03626011 ; Sociologie. École normale supérieure, 2017 (2017) BASE Show details Page: 1 2 3 4 5 6 7 8 9 10...48

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