Cette étude porte sur les différentes formalisations du sens linguistique dans le cadre de la théorie Sens-Texte (TST) ; elle s’intéresse notamment à la représentation sémantique, la représentation la plus importante dans la modélisation du sens d’énoncés et d’unités lexicales de la langue. Cette étude a trois objectifs : premièrement, décrire trois formalisations de la représentation sémantique existant dans la TST – les réseaux sémantiques, les définitions lexicographiques et les définitions de la BDéf (des définitions très formalisées) ; deuxièmement, identifier les disparités et les décalages entre les formalisations choisies ; troisièmement, proposer des modifications aux formalisations existantes pour réduire au maximum ces disparités. / This study explores the different formalisations of linguistic meaning as encountered in the Meaning-Text Theory (MST) framework. Its main interest is semantic representation, which is the most important representation in the modeling of the meaning of utterances and lexical units. This study has three objectives: first, describing three existing formalisations of semantic representation in the MST – semantic networks, lexicographical definitions, and BDéf definitions (definitions that are extremely formalized); second, identifying the discrepancies between these formalisations; finally, proposing modifications to these formalisations in order to reduce the disparities as much as possible.

Recent modeling work shows that patterns of shared perceptual features relate to the group-level order of acquisition of early-learned words (Peters & Borovsky, 2019). Here we present results for two eye-tracked word recognition studies showing patterns of shared perceptual features likewise influence processing of known and novel noun-concepts in individual 24- to 30-month-old toddlers. In the first study (Chapter 2, N=54), we explored the influence of perceptual connectivity on both initial attentional biases to known objects and subsequent label processing. In the second study (Chapter 3, N=49), we investigated whether perceptual connectivity influences patterns of attention during learning opportunities for novel object-features and object-labels, subsequent pre-labeling attentional biases, and object-label learning outcomes. Results across studies revealed four main findings. First, patterns of shared (visual-motion and visual-form and surface) perceptual features do relate to differences in early noun-concept processing at the individual level. Second, such influences are tentatively at play from the outset of novel noun-concept learning. Third, connectivity driven attentional biases to both recently learned and well-known objects follow a similar timecourse and show similar patterns of individual differences. Fourth, initial, pre-labeling attentional biases to objects relate to subsequent label processing, but do not linearly explain effects of connectivity. Finally, we consider whether these findings provide support for shared-feature-guided selective attention to object features as a mechanism underlying early lexico-semantic development.

Made available in DSpace on 2015-03-05T18:10:48Z (GMT). No. of bitstreams: 0 Previous issue date: 26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A meta principal desta pesquisa é realizar um estudo da semântica dos adjetivos objetivando a sua representação em uma ontologia jurídica. O fato motivador desse interesse foi o estudo de ontologias e léxicos computacionais jurídicos e a constatação de que os adjetivos não recebem um tratamento sistemático nessas ferramentas computacionais de conhecimento especializado. A partir dessa constatação, partiu-se para o estudo de ontologias e léxicos computacionais de linguagem não-especializada, buscando elementos práticos e teóricos para a inclusão sistemática, e não ocasional, de adjetivos em uma ontologia jurídica. Esta pesquisa defende que a eficiência no tratamento computacional da linguagem requer uma combinação de teorias. Assim, a metodologia adotada combina diferentes abordagens teóricas. Através do estudo do corpus e construção da ontologia jurídica, percebe-se a importância dos adjetivos para a organização do conhecimento especializado. Nos domínios de conhecimento especializados, os adjetivos têm a fu / The main goal of this research is to study the semantics of the adjectives in order to codify them into a legal ontology. Through a search for legal ontologies and lexicons one verifies that adjectives are not codified systematically into specialized lexicons and ontologies. Taking the codification of adjectives into a legal ontology as the target of this work, one analyzes non-specialized language ontologies and lexicons. The purpose of such analysis is to find practical and theoretical elements for including adjectives systematically into a legal ontology. This research defends the position that integrating linguistic approaches is more fruitful to natural language processing. Therefore, the methodology applied here combines different theoretical approaches. The corpus analysis and legal ontology construction shows the importance of adjectives in organizing the specialized knowledge. In the specialized domains, adjectives have the main function of classifying entities

The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro-/MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher-level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most suitable of several connectivity metrics, we utilised principal component analysis (PCA) to find commonalities and differences of those methods when applied to a dataset and identified the most suitable metric based on the maximum explained variance. Secondly, reconstruction of EEG/MEG connectomes using anatomical or fMRI-based parcellations can be significantly contaminated by spurious leakage-induced connections in source space. We, therefore, utilised cross-talk functions in order to optimise the number, size and locations of cortical parcels, obtaining EEG/MEG-adaptive parcellations. In summary, this thesis proposes approaches for optimising EEG/MEG connectivity analyses and applies them to provide the first empirical evidence regarding some of the core predictions of the hub-and-spokes model. The key findings support the general framework of the hub(s)-and-spokes, but also suggest modifications to the model, particularly regarding the definition of semantic hub(s).

Η βελτίωση ερωτημάτων (Query refinement) είναι η διαδικασία πρότασης εναλλακτικών όρων στους χρήστες των μηχανών αναζήτησης του Διαδικτύου για την διατύπωση της πληροφοριακής τους ανάγκης. Παρόλο που εναλλακτικοί σχηματισμοί ερωτημάτων μπορούν να συνεισφέρουν στην βελτίωση των ανακτηθέντων αποτελεσμάτων, η χρησιμοποίησή τους από χρήστες του Διαδικτύου είναι ιδιαίτερα περιορισμένη καθώς οι όροι των βελτιωμένων ερωτημάτων δεν περιέχουν σχεδόν καθόλου πληροφορία αναφορικά με τον βαθμό ομοιότητάς τους με τους όρους του αρχικού ερωτήματος, ενώ συγχρόνως δεν καταδεικνύουν το βαθμό συσχέτισής τους με τα πληροφοριακά ενδιαφέροντα των χρηστών. Παραδοσιακά, οι εναλλακτικοί σχηματισμοί ερωτημάτων καθορίζονται κατ’ αποκλειστικότητα από τη σημασιολογική σχέση που επιδεικνύουν οι συμπληρωματικοί όροι με τους αρχικούς όρους του ερωτήματος, χωρίς να λαμβάνουν υπόψη τον επιδιωκόμενο στόχο της αναζήτησης που υπολανθάνει πίσω από ένα ερώτημα του χρήστη. Στην παρούσα εργασία θα παρουσιάσουμε μια πρότυπη τεχνική βελτίωσης ερωτημάτων η οποία χρησιμοποιεί μια λεξική οντολογία προκειμένου να εντοπίσει εναλλακτικούς σχηματισμούς ερωτημάτων οι οποίοι αφενός, θα περιγράφουν το αντικείμενο της αναζήτησης του χρήστη και αφετέρου θα σχετίζονται με τα ερωτήματα που υπέβαλε ο χρήστης. Το πιο πρωτοποριακό χαρακτηριστικό της τεχνικής μας είναι η οπτική αναπαράσταση του εναλλακτικού ερωτήματος με την μορφή ενός ιεραρχικά δομημένου γράφου. Η αναπαράσταση αυτή παρέχει σαφείς πληροφορίες για την σημασιολογική σχέση μεταξύ των όρων του βελτιωμένου ερωτήματος και των όρων που χρησιμοποίησε ο χρήστης για να εκφράσει την πληροφοριακή του ανάγκη ενώ παράλληλα παρέχει την δυνατότητα στον χρήστη να επιλέξει ποιοι από τους υποψήφιους όρους θα συμμετέχουν τελικά στην διαδικασία βελτιστοποίησης δημιουργώντας διαδραστικά το νέο ερώτημα. Τα αποτελέσματα των πειραμάτων που διενεργήσαμε για να αξιολογήσουμε την απόδοση της τεχνικής μας, είναι ιδιαίτερα ικανοποιητικά και μας οδηγούν στο συμπέρασμα ότι η μέθοδός μας μπορεί να βοηθήσει σημαντικά στη διευκόλυνση του χρήστη κατά τη διαδικασία επιλογής ερωτημάτων για την ανάκτηση πληροφορίας από τα δεδομένα του Παγκόσμιου Ιστού. / Query refinement is the process of providing Web information seekers with alternative wordings for expressing their information needs. Although alternative query formulations may contribute to the improvement of retrieval results, nevertheless their realization by Web users is intrinsically limited in that alternative query wordings do not convey explicit information about neither their degree nor their type of correlation to the user-issued queries. Moreover, alternative query formulations are determined based on the semantics of the issued query alone and they do not consider anything about the search intentions of the user issuing that query. In this paper, we introduce a novel query refinement technique which uses a lexical ontology for identifying alternative query formulations that are both informative of the user’s interests and related to the user selected queries. The most innovative feature of our technique is the visualization of the alternative query wordings in a graphical representation form, which conveys explicit information about the refined queries correlation to the user issued requests and which allows the user select which terms to participate in the refinement process. Experimental results demonstrate that our method has a significant potential in improving the user search experience.

Cette étude porte sur les différentes formalisations du sens linguistique dans le cadre de la théorie Sens-Texte (TST) ; elle s’intéresse notamment à la représentation sémantique, la représentation la plus importante dans la modélisation du sens d’énoncés et d’unités lexicales de la langue. Cette étude a trois objectifs : premièrement, décrire trois formalisations de la représentation sémantique existant dans la TST – les réseaux sémantiques, les définitions lexicographiques et les définitions de la BDéf (des définitions très formalisées) ; deuxièmement, identifier les disparités et les décalages entre les formalisations choisies ; troisièmement, proposer des modifications aux formalisations existantes pour réduire au maximum ces disparités. / This study explores the different formalisations of linguistic meaning as encountered in the Meaning-Text Theory (MST) framework. Its main interest is semantic representation, which is the most important representation in the modeling of the meaning of utterances and lexical units. This study has three objectives: first, describing three existing formalisations of semantic representation in the MST – semantic networks, lexicographical definitions, and BDéf definitions (definitions that are extremely formalized); second, identifying the discrepancies between these formalisations; finally, proposing modifications to these formalisations in order to reduce the disparities as much as possible.

Comment les structures sociales et sémantiques d'une communauté scientifique guident-elles les dynamiques de collaboration à venir ? Dans cette thèse, nous combinons des techniques de traitement automatique des langues et des méthodes provenant de l'analyse de réseaux complexes pour analyser une base de données de publications scientifiques dans le domaine de la linguistique computationnelle : l'ACL Anthology. Notre objectif est de comprendre le rôle des collaborations entre les chercheurs dans la construction du paysage sémantique du domaine, et, symétriquement, de saisir combien ce même paysage influence les trajectoires individuelles des chercheurs et leurs interactions. Nous employons des outils d’analyse du contenu textuel pour extraire des textes des publications les termes correspondant à des concepts scientifiques. Ces termes sont ensuite connectés aux chercheurs pour former un réseau socio-sémantique, dont nous modélisons la dynamique à différentes échelles. Nous construisons d’abord un modèle statistique, à base de régressions logistiques multivariées, qui permet de quantifier le rôle respectif des propriétés sociales et sémantiques de la communauté sur la dynamique microscopique du réseau socio-sémantique. Nous reconstruisons par la suite l’évolution du champ de la linguistique computationelle en créant différentes cartographies du réseau sémantique, représentant les connaissances produites dans le domaine, mais aussi le flux d’auteurs entre les différents champs de recherche du domaine. En résumé, nos travaux ont montré que la combinaison des méthodes issues du traitement automatique des langues et de l'analyse des réseaux complexes permet d'étudier d'une manière nouvelle l'évolution des domaines scientifiques. / How are the social and semantic structures of a scientific communitydriving future research dynamics? In this thesis we combine naturallanguage processing techniques and network theory methods to analyzeavery large dataset of scientific publications in the field of computationallinguistics,i.e.the ACL Anthology. Ultimately, our goal is to understandthe role of collaborations among researchers in building and shaping thelandscape of scientific knowledge, and, symmetrically, to understand howthe configuration of this landscape influences individual trajectories ofresearchers and their interactions. We use natural language processingtools to extract the terms corresponding to scientific concepts from thetexts of the publications. Then we reconstruct a socio-semantic networkconnecting researchers and scientific concepts, and model the dynamicsof its evolution at different scales. To achieve this, we first build astatistical model, based on multivariate logistic regression, that quantifiesthe role that social and semantic features play in the evolution of thesocio-semantic network, namely in the emergence of new links. Then, wereconstruct the evolution of the field through different visualizations of theknowledge produced therein, and of the flow of researchers across thedifferent subfields of the domain. To summarize, we have shown throughour work that the combination of natural language processing techniqueswith complex network analysis makes it possible to investigate in a novelway the evolution of scientific fields.

Submitted by Marcos Grilo Rosa (grilo@uefs.br) on 2017-01-05T19:11:59Z No. of bitstreams: 1 tesevsfinalMarcosGrilo.pdf: 11311408 bytes, checksum: 3ce07aff3fe0fb9dc534e80e667eeb5b (MD5) / Rejected by Maria Auxiliadora da Silva Lopes (silopes@ufba.br), reason: Prezado Marcos, Favor registrar os nomes completos dos participantes da banca; inserir o "abstract" e as "key words". Saudações, Maria Auxiliadora Lopes UFBA/Faced/Biblioteca on 2017-01-09T14:10:18Z (GMT) / Submitted by Marcos Grilo Rosa (grilo@uefs.br) on 2017-01-09T17:16:07Z No. of bitstreams: 1 tesevsfinalMarcosGrilo.pdf: 11311408 bytes, checksum: 3ce07aff3fe0fb9dc534e80e667eeb5b (MD5) / Approved for entry into archive by Maria Auxiliadora da Silva Lopes (silopes@ufba.br) on 2017-01-19T14:35:53Z (GMT) No. of bitstreams: 1 tesevsfinalMarcosGrilo.pdf: 11311408 bytes, checksum: 3ce07aff3fe0fb9dc534e80e667eeb5b (MD5) / Made available in DSpace on 2017-01-19T14:35:53Z (GMT). No. of bitstreams: 1 tesevsfinalMarcosGrilo.pdf: 11311408 bytes, checksum: 3ce07aff3fe0fb9dc534e80e667eeb5b (MD5) / No campo da inteligência artificial, redes semânticas são utilizadas para a representação de conhecimento e processamento de linguagem natural. Redes semânticas são sistemas de representação do conhecimento baseados em grafos cujos vértices são palavras e as arestas, os relacionamentos entre as palavras estabelecidos por alguma regra. Lidar com redes semânticas é uma atividade bastante complicada, principalmente, quando se manipula redes com milhares de palavras. Conjecturamos a possibilidade de se representar uma dada rede semântica baseada em títulos de artigos de periódicos científicos (RST) por meio de uma RST uniforme, de modo que esta possua a mesma estrutura topológica e reflita o vocabulário da RST original. O problema da nossa tese consiste em saber qual o menor tamanho de uma RST uniforme que pode ser obtida de uma dada RST. Esta pesquisa visa determinar o ponto crítico no qual ocorre uma mudança topológica de uma RST de modo que a rede obtida reflita o vocabulário da RST original. Realizamos um experimento que consistiu em submeter RST a estratégias de remoção de vértices. Os resultados apontam a existência de uma fração fp de vértices removidos aleatoriamente e uniformemente que mantém a estrutura topológica e reflita o vocabulário da RST original. Desta forma, RST podem ser representadas por RST uniformes mínimas. A representação do conhecimento é determinante em processos e cientes de busca de informações e difusão de conhecimento. Ademais, RST uniformes mínimas poderão auxiliar pesquisadores na busca mais e ciente de temas relacionados ao seu objeto de investigação. / Abstract In the field of artificial intelligence, semantic networks are used for knowledge representation and natural language processing. Semantic networks are graph-based knowledge representation systems whose vertices are words and edges, relationships between words set by a rule. Dealing with semantic networks is a very complicated activity, mainly, when it handles networks with thousands of words. We conjecture the possibility of representing a given semantic network based on titles of scientific papers (RST) by a uniform RST, so that this has the same topological structure and reflect the vocabulary of the original RST. The problem of our thesis is to know what a uniform smaller RST can be obtained from a given RST. This study aims to determine the critical point at which there is a topological change of a RST so that the network obtained reflect the vocabulary of the original RST. We conducted an experiment that it consists to submit RST removing vertices strategies. The results indicate the existence of a fraction fp vertices removed randomly and evenly preserving the topological structure and reflect the vocabulary of the original RST. Thus, RST may be represented by minimum uniform RST. The representation of knowledge is determinant in efficient processes search of information and diffusion of knowledge. In addition, minimum uniform RST can assist researchers in the most efficient search issues related to its investigation.

Cette recherche propose une analyse des usages des systèmes de tagging sur le Web. Au delà d'un simple outil personnel de « catégorisation ordinaire » des ressources en ligne, les classifications produites à partir de l'indexation libre de tags, nommées folksonomies, permettent aux internautes de produire des « prises » physiques et interprétatives quideviennent un support aux formes de navigation sociale. Ces réseaux de traces numériques permettent aux internautes de se repérer et d'agir dans l'univers complexe et abondant que représente le Web, mais elles sont également un support de coordination avec d'autres internautes. Les tags apparaissent comme des appuis conventionnels qui permettent decoordonner les actions au sein de collectifs à géométrie variable, plus ou moins étendus et identifiés, dont les membres partagent des centres d’intérêts et un vocabulaire commun. A partir d'une approche socio-technique nous étudions le couplage entre conception et usage de ces dispositifs, et montrons que le tagging propose une voie alternative, encore enconstruction, aux politiques existantes d'architecture et d’accessibilité des ressources du Web / This research proposes an anlysis of tagging systems uses on the Web. Tagging is not a simple personnal tool of web ressources categorization but classifications emerging from free tags indexation, named folksonomies, allow Internet users to create physical and interpretative supports which produce new forms of social navigation. Those tagging networks give the opportunity to Internet users to navigate and to act in the complex and abundant Web universe, but they are also coordination supports with other Internet users. Tags appear as conventionnal supports which allow coordination of actions in variable-geometry collectives. In those more or less identified and large collectives, members share common vocabulary and interests. Based on a socio-technical approach, we study coupling between conception and uses of tagging systems. We explain that tagging proposes an under construction and alternative way to architecture and accessibility politics of the Web ressources

We describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts). The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between related concepts. Our work can thus be conceived of as augmenting the WordNet noun ontology with RelatedTo links. The network, which we refer to as the Szumlanski-Gomez Network (SGN), has been subjected to a variety of evaluative measures, including manual inspection by human judges and quantitative comparison to gold standard data for semantic relatedness measurements. We have also evaluated the network’s performance in an applied setting on a word sense disambiguation (WSD) task in which the network served as a knowledge source for established graph-based spreading activation algorithms, and have shown: a) the network is competitive with WordNet when used as a stand-alone knowledge source for WSD, b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually, and c) our network outperforms a similar resource, WordNet++ (Ponzetto & Navigli, 2010), that has been automatically derived from annotations in the Wikipedia corpus. iii Finally, we present a study on human perceptions of relatedness. In our study, we elicited quantitative evaluations of semantic relatedness from human subjects using a variation of the classical methodology that Rubenstein and Goodenough (1965) employed to investigate human perceptions of semantic similarity. Judgments from individual subjects in our study exhibit high average correlation to the elicited relatedness means using leave-one-out sampling (r = 0.77, σ = 0.09, N = 73), although not as high as average human correlation in previous studies of similarity judgments, for which Resnik (1995) established an upper bound of r = 0.90 (σ = 0.07, N = 10). These results suggest that human perceptions of relatedness are less strictly constrained than evaluations of similarity, and establish a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We also contrast the performance of a variety of similarity and relatedness measures on our dataset to their performance on similarity norms and introduce our own dataset as a supplementary evaluative standard for relatedness measures.