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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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VEREINDEUTIGUNG ZUR KLASSIFIZIERUNG LEXIKALISCHER OBJEKTE ; DISAMBIGUATION FOR THE CLASSIFICATION OF LEXICAL ITEMS ; DÉSAMBÏGUISATION POUR LA CLASSIFICATION DE LEXÈMES
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In: https://hal.archives-ouvertes.fr/hal-03598242 ; France, Patent n° : EP3937059A1. 2022 (2022)
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PROTECT: A Pipeline for Propaganda Detection and Classification
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In: CLiC-it 2021- Italian Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03417019 ; CLiC-it 2021- Italian Conference on Computational Linguistics, Jan 2022, Milan, Italy (2022)
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Machine infelicity in a poignant visitor setting: Comparing human and AI’s ability to analyze discourse
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In: Research outputs 2014 to 2021 (2022)
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Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 5; Pages: 2952 (2022)
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A Comparative Review on Applications of Different Sensors for Sign Language Recognition
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In: Journal of Imaging; Volume 8; Issue 4; Pages: 98 (2022)
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A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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Cultural Intelligence in the Study of Intelligence
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In: Journal of Strategic Security (2022)
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Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020
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| 11 |
Психофизиологические механизмы, лежащие в основе процесса восприятия речи ; Psychophysiological Mechanisms of the Speech Perception Process
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cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations
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In: Erofeev, Gleb, Sorokina, Irina, Han, Lifeng orcid:0000-0002-3221-2185 and Gladkoff, Serge (2021) cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations. In: Machine Translation Summit 2021, 16-20 Aug 2021, USA (online). (In Press) (2021)
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Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement
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In: Alekseeva, Alexandra orcid:0000-0002-7990-4592 , Gladkoff, Serge, Sorokina, Irina and Han, Lifeng orcid:0000-0002-3221-2185 (2021) Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement. In: Metrics 2021: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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Proactive information retrieval
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Sen, Procheta. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
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In: Sen, Procheta (2021) Proactive information retrieval. PhD thesis, Dublin City University. (2021)
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Sentiment Analysis of Arabic Documents
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In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
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| 17 |
On Refining BERT Contextualized Embeddings using Semantic Lexicons
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In: Machine Learning with Symbolic Methods and Knowledge Graphs co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021) ; https://hal.archives-ouvertes.fr/hal-03318571 ; Machine Learning with Symbolic Methods and Knowledge Graphs co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), Sep 2021, Online, Spain (2021)
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| 18 |
Ein Überblick über die neuesten abstrakten Zusammenfassungstechniken ; A Survey of Recent Abstract Summarization Techniques ; Un aperçu des techniques récentes de résumé abstrait
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In: Proceedings of Sixth International Congress on Information and Communication TechnologyICICT 2021, London, Volume 4Series: Lecture Notes in Networks and Systems, Vol. 217Yang, X.-S., Sherratt, S., Dey, N., Joshi, A. (Eds.) 2021 ; Proceedings of Sixth International Congress on Information and Communication Technology ICICT 2021, London, Volume 4, Series: Lecture Notes in Networks and Systems, Vol. 217. Springer Singapore, 2021 ; https://hal.archives-ouvertes.fr/hal-03216381 ; Proceedings of Sixth International Congress on Information and Communication Technology ICICT 2021, London, Volume 4, Series: Lecture Notes in Networks and Systems, Vol. 217. Springer Singapore, 2021, ICICT 2021, Feb 2021, London, United Kingdom ; https://www.waterstones.com/book/proceedings-of-sixth-international-congress-on-information-and-communication-technology/xin-she-yang/simon-sherratt/9789811621017 (2021)
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Abstract:
International audience ; In diesem Artikel werden einige neuere abstrakte Zusammenfassungsmethoden vorgestellt: T5, Pegasus und ProphetNet. Wir implementieren die Systeme in zwei Sprachen: Englisch und Indonesisch. Wir untersuchen die Auswirkungen von Pre-Training-Modellen (ein T5, drei Pegasuses, drei ProphetNets) auf mehrere Wikipedia-Datensätze in englischer und indonesischer Sprache und vergleichen die Ergebnisse mit den Zusammenfassungen der Wikipedia-Systeme. Das T5-Large, das Pegasus-XSum und das ProphetNet-CNNDM bieten die beste Zusammenfassung. Die wichtigsten Faktoren, die die ROUGE-Leistung beeinflussen, sind Abdeckung, Dichte und Komprimierung. Je höher die Punktzahl, desto besser die Zusammenfassung. Weitere Faktoren, die die ROUGE-Werte beeinflussen, sind das Ziel vor dem Training, die Merkmale des Datensatzes, der Datensatz, der zum Testen des vorab trainierten Modells verwendet wird, und die mehrsprachige Funktion. Einige Vorschläge zur Verbesserung der Einschränkung dieses Dokuments sind: 1) Sicherstellen, dass der für das Modell vor dem Training verwendete Datensatz ausreichend groß sein muss und angemessene Instanzen für die Behandlung von mehrsprachigen Zwecken enthält; 2) Ein fortgeschrittener Prozess (Feinabstimmung) muss angemessen sein. Wir empfehlen, den großen Datensatz zu verwenden, der eine umfassende Abdeckung von Themen aus vielen Sprachen umfasst, bevor fortgeschrittene Prozesse wie das Train-Infer-Train-Verfahren zur Zero-Shot-Übersetzung in der Trainingsphase des Pre-Training-Modells implementiert werden. ; This paper surveys several recent abstract summarization methods: T5, Pegasus, and ProphetNet. We implement the systems in two languages: English and Indonesian languages. We investigate the impact of pre-training models (one T5, three Pegasuses, three ProphetNets) on several Wikipedia datasets in English and Indonesian language and compare the results to the Wikipedia systems' summaries. The T5-Large, the Pegasus-XSum, and the ProphetNet-CNNDM provide the best summarization. The most significant factors that influence ROUGE performance are coverage, density, and compression. The higher the scores, the better the summary. Other factors that influence the ROUGE scores are the pre-training goal, the dataset's characteristics, the dataset used for testing the pre-trained model, and the cross-lingual function. Several suggestions to improve this paper's limitation are: 1) assure that the dataset used for the pre-training model must sufficiently large, contains adequate instances for handling cross-lingual purpose; 2) Advanced process (finetuning) shall be reasonable. We recommend using the large dataset consists of comprehensive coverage of topics from many languages before implementing advanced processes such as the train-infer-train procedure to the zero-shot translation in the training stage of the pre-training model. ; Cet article examine plusieurs méthodes récentes de résumé des résumés: T5, Pegasus et ProphetNet. Nous implémentons les systèmes en deux langues: anglais et indonésien. Nous étudions l'impact des modèles de pré-formation (un T5, trois Pegasus, trois ProphetNets) sur plusieurs ensembles de données Wikipédia en anglais et en indonésien et comparons les résultats aux résumés des systèmes Wikipédia. Le T5-Large, le Pegasus-XSum et le ProphetNet-CNNDM fournissent le meilleur résumé. Les facteurs les plus importants qui influencent les performances de ROUGE sont la couverture, la densité et la compression. Plus les scores sont élevés, meilleur est le résumé. D'autres facteurs qui influencent les scores ROUGE sont l'objectif de pré-formation, les caractéristiques de l'ensemble de données, l'ensemble de données utilisé pour tester le modèle pré-entraîné et la fonction multilingue. Plusieurs suggestions pour améliorer les limites de cet article sont: 1) s'assurer que l'ensemble de données utilisé pour le modèle de pré-formation doit être suffisamment grand, contient des instances adéquates pour gérer l'objectif multilingue; 2) Le processus avancé (réglage fin) doit être raisonnable. Nous vous recommandons d'utiliser le grand ensemble de données qui consiste en une couverture complète de sujets dans de nombreuses langues avant de mettre en œuvre des processus avancés tels que la procédure train-infer-train à la traduction zéro-shot dans la phase de formation du modèle de pré-formation.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; abstract summarization; ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL; ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and Indexing/H.3.1.0: Abstracting methods; cross-lingual system; Pegasus; ProphetNet; T5; train-infer-train; Transformers
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URL: https://hal.archives-ouvertes.fr/hal-03216381/document
https://hal.archives-ouvertes.fr/hal-03216381/file/2105.00824_DiyahPuspitaningrum_arXiv.pdf
https://hal.archives-ouvertes.fr/hal-03216381
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| 19 |
Dataset of coronavirus content from Instagram with an exploratory analysis
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559489 ; IEEE Access, IEEE, 2021, 9, pp.157192-157202. ⟨10.1109/ACCESS.2021.3126552⟩ (2021)
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Learning a weather dictionary of atmospheric patterns using Latent Dirichlet Allocation
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In: https://hal.archives-ouvertes.fr/hal-03258523 ; 2021 (2021)
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