940 resultados para NLP (Natural Language Processing)


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This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

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Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity. Hum Brain Mapp 30:452-461, 2009. (C) 2007 Wiley-Liss, Inc.

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This paper examines the effects of information request ambiguity and construct incongruence on end user's ability to develop SQL queries with an interactive relational database query language. In this experiment, ambiguity in information requests adversely affected accuracy and efficiency. Incongruities among the information request, the query syntax, and the data representation adversely affected accuracy, efficiency, and confidence. The results for ambiguity suggest that organizations might elicit better query development if end users were sensitized to the nature of ambiguities that could arise in their business contexts. End users could translate natural language queries into pseudo-SQL that could be examined for precision before the queries were developed. The results for incongruence suggest that better query development might ensue if semantic distances could be reduced by giving users data representations and database views that maximize construct congruence for the kinds of queries in typical domains. (C) 2001 Elsevier Science B.V. All rights reserved.

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Radio Link Quality Estimation (LQE) is a fundamental building block for Wireless Sensor Networks, namely for a reliable deployment, resource management and routing. Existing LQEs (e.g. PRR, ETX, Fourbit, and LQI ) are based on a single link property, thus leading to inaccurate estimation. In this paper, we propose F-LQE, that estimates link quality on the basis of four link quality properties: packet delivery, asymmetry, stability, and channel quality. Each of these properties is defined in linguistic terms, the natural language of Fuzzy Logic. The overall quality of the link is specified as a fuzzy rule whose evaluation returns the membership of the link in the fuzzy subset of good links. Values of the membership function are smoothed using EWMA filter to improve stability. An extensive experimental analysis shows that F-LQE outperforms existing estimators.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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Dissertação para obtenção do Grau de Doutor em Engenharia Informática

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In the field of energy, natural gas is an essential bridge to a clean, low carbon, renewable energy era. However, natural gas processing and transportation regulation require the removal of contaminant compounds such as carbon dioxide (CO2). Regarding clean air, the increasing atmospheric concentrations of greenhouse gases, specifically CO2, is of particular concern. Therefore, new costeffective, high performance technologies for carbon capture have been researched and the design of materials with the ability to efficiently separate CO2 from other gases is of vital importance.(...)

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Introduction. In autism and schizophrenia attenuated/atypical functional hemispheric asymmetry and theory of mind impairments have been reported, suggesting common underlying neuroscientific correlates. We here investigated whether impaired theory of mind performance is associated with attenuated/atypical hemispheric asymmetry. An association may explain the co-occurrence of both dysfunctions in psychiatric populations. Methods. Healthy participants (n 129) performed a left hemisphere (lateralised lexical decision task) and right hemisphere (lateralised face decision task) dominant task as well as a visual cartoon task to assess theory of mind performance. Results. Linear regression analyses revealed inconsistent associations between theory of mind performance and functional hemisphere asymmetry: enhanced theory of mind performance was only associated with (1) faster right hemisphere language processing, and (2) reduced right hemisphere dominance for face processing (men only). Conclusions. The majority of non-significant findings suggest that theory of mind and functional hemispheric asymmetry are unrelated. Instead of ''overinterpreting'' the two significant results, discrepancies in the previous literature relating to the problem of the theory of mind concept, the variety of tasks, and the lack of normative data are discussed. We also suggest how future studies could explore a possible link between hemispheric asymmetry and theory of mind.

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En el treball es realitza una transcripció de dos programes de televisió, amb la idea de saber quin és el tipus de llenguatge que usen aquests mitjans per adreçar-se al seu públic. Però seria absurd ignorar altres canals per als quals la llengua és imprescindible. Em refereixo al cinema, sobretot. I malgrat que no es considera un mitjà de comunicació, també és un element importantíssim pel que fa al tractament i transmissió lingüístics. I molts productes del cinema acaben sortint per televisió. La premsa escrita i, com a cas especial, Internet, també hi tenen força a dir.

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Peer-reviewed

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This paper analyzes and evaluates, in the context of Ontology learning, some techniques to identify and extract candidate terms to classes of a taxonomy. Besides, this work points out some inconsistencies that may be occurring in the preprocessing of text corpus, and proposes techniques to obtain good terms candidate to classes of a taxonomy.

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Background: Language processing abnormalities and inhibition difficulties are hallmark features of schizophrenia. The objective of this study is to asses the blood oxygenation level-dependent (BOLD) response at two different stages of the illness and compare the frontal activity between adolescents and adults with schizophrenia. Methods: 10 adults with schizophrenia (mean age 31,5 years) and 6 psychotic adolescents with schizophrenic symptoms (mean age 16,2 years) underwent functional magnetic resonance imaging while performing two frontal tasks. Regional activation is compared in the bilateral frontal areas during a covert verbal fluency task (letter version) and a Stroop task (inhibition task). Results: Preliminary results show poorer task performance and less frontal cortex activation during both tasks in the adult group of patients with schizophrenia. In the adolescent patients group, fMRI analysis show significant and larger activity in the left frontal operculum (Broca's area) in the verbal fluency task and greater activity in the medium cingulate during the inhibition phase of the Stroop task. Conclusions: These preliminary findings suggest a decrease of frontal activity in the course of the illness. We assume that schizophrenia contributes to frontal brain activity reduction.