Search results for " Natural Language Processing"

showing 4 items of 14 documents

Intelligent Agents supporting user interactions within self regulated learning processes

2010

The paper focuses on the main advantages in the defnition and utilization of an open and modular e-learning software platform to support highly cognitive tasks performed by the main actors of the learning process. We present in detail the integration inside the platform of two intelligent agents devoted to talking with the student and to retrieving new information sources on the Web. The process is triggered as a reply to the system’s perception that the student feels discontented with the presented contents. The architecture is detailed, and some conclusions about the growth of the platform’s overall performance are expressed.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionilcsh:Theory and practice of educationSelf Regulated LearningLearning Management SystemsNatural language processingMulti Agent SystemsMulti Agent Systems Learning Management Systems Self Regulated Learning Natural language processinglcsh:LB5-3640
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Fake News Spreaders Detection: Sometimes Attention Is Not All You Need

2022

Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the reference multilingual dataset for the considered task, exploiting corpus linguistics techniques, such as chi-square test, keywords and Word Sketch. Second, we perform experiments on several models for Natural Language Processing. Third, we perform a comparative evaluation using the most recent Transformer-based models (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) and other deep and non-deep SotA models (CNN,…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionitext classificationcorpus linguisticSettore ING-INF/03 - Telecomunicazionifake newTwitterauthor profilingconvolutional neural networkdeep learningNatural Language Processing (NLP)user classificationfake news; misinformation; Natural Language Processing (NLP); transformers; Twitter; convolutional neural networks; text classification; deep learning; machine learning; user classification; author profiling; corpus linguistics; linguistic analysismachine learningtransformermisinformationlinguistic analysisInformation Systems
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Le grand débat national, une aide pour prendre des décisions locales?

2021

The Great National Debate, decided by Emmanuel Macron at the beginning of 2019 to respond to the Yellow Vests social movement, allowed the collection of citizens’ contributions on the ecological transition via an online platform. In this article, we use the corpus constituted by these contributions to identify areas where participants are asking for the development of bicycle paths and railway facilities. For this purpose, we have created a classification model to identify contributions dealing with the theme of transportation and proposed a method for extracting patterns that reflect the contributors’ proposals. We then represented these patterns on maps, using the contributors’ postal cod…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing/I.2.7.0: DiscourseMotifs[SHS.GEO] Humanities and Social Sciences/GeographyGrand Débat NationalTransport[SHS.GEO]Humanities and Social Sciences/GeographyPatternsACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Where lol is: function and position of lol used as a discourse marker in YouTube comments

2020

Lol is probably one of the most popular words in computer-mediated communication. It is generally taken to be the acronym of “laughing out loud”, but it is not always used to indicate a humorous response; rather, it is multifunctional. Drawing on previous studies of the different functions of lol, this paper explores a possible correlation between the position and function of non-lexicalized lol in the specific context of YouTube comments. The hypothesis is that the function of lol largely depends on its position: clause-initial lol is not used with the same functions as clause-final lol. The data for the study come from the comment threads of three popular YouTube videos posted in 2017, 20…

media_common.quotation_subjectDiscourse analysis[SHS.INFO]Humanities and Social Sciences/Library and information scienceslolContext (language use)[SHS.INFO] Humanities and Social Sciences/Library and information sciencesmarqueur pragmatique030507 speech-language pathology & audiology03 medical and health scienceslcsh:P1-1091Acronym[SHS.LANGUE]Humanities and Social Sciences/LinguisticsFunction (engineering)discours médié par ordinateurmedia_commondiscourse marker060201 languages & linguisticsposition syntaxiqueYouTubemarqueur discursiflcsh:P98-98.506 humanities and the artsPragmatics[SHS.LANGUE] Humanities and Social Sciences/LinguisticsLinguisticslcsh:Philology. Linguistics0602 languages and literaturecomputer-mediated discoursepragmatic markerlcsh:Computational linguistics. Natural language processing0305 other medical sciencePsychologysyntactic positionDiscourse marker
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