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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cazabet, Rémy; Citraro, Salvatore; Rossetti, Giulio;

    Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this article, we introduce a framework for the generation of random graphs with a controlled size -- number of nodes, edges -- and a customizable structure, beyond blocks and spatial ones, based on node-pair rank and a tunable probability function allowing to control the amount of randomness. We introduce a structure zoo -- a collection of original network structures -- and conduct experiments on the small-world properties of networks generated by those structures. Finally, we introduce an implementation as a Python library named Structify-net. Comment: Accepted version at PCI network science

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HAL-ENS-LYON; INRIA ...arrow_drop_down
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    https://doi.org/10.48550/arxiv...
    Article . 2023
    License: arXiv Non-Exclusive Distribution
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  • Authors: Zervakis, Georgios;

    Recent advances in deep learning and neural networks have made it possible to address complex natural language processing tasks, which find application in a plethora of real-world problems ranging from smart assistants in mobile devices to the prediction of cancer. Nonetheless, modern systems based on these frameworks exhibit various limitations that may compromise their performance and trustworthiness, render them unfair towards minorities, or subject them to privacy leakage. It is our belief that integrating symbolic knowledge and reasoning into the deep learning framework is a necessary step towards addressing the aforementioned limitations. For example, lexical resources can enrich deep neural networks with semantic or syntactic knowledge, and logical rules can provide learning and reasoning mechanisms. Therefore, the scope of this thesis is to develop and evaluate ways of integrating different types of symbolic knowledge and reasoning into a widely used language model, Bidirectional Encoder Representations from Transformers (BERT). ln a first stage, we consider retrofitting, a simple and popular technique for refining distributional word embeddings based on relations coming from a semantic lexicon. Inspired by this technique, we present two methods for incorporating this knowledge into BERT contextualized embeddings. We evaluate these methods on three biomedical datasets for relation extraction and one movie review dataset for sentiment analysis, and show that they do not substantially impact the performance for these tasks. Furthermore, we conduct a qualitative analysis to provide further insights on this negative result. ln a second stage, we integrate analogical reasoning with BERT as a means to improve its performance on the target sense verification task, and make it more robust. To do so, we reformulate target sense verification as an analogy detection task. We present a hybrid model that combines BERT to encode the input data into quadruples and a convolutional neural classifier to decide whether they constitute valid analogies. We test our system on a benchmark dataset, and show that it can outperform existing approaches. Our empirical study shows the importance of the input encoding for BERT, and how this dependence gets alleviated by integrating the axiomatic properties of analogies during training, while preserving performance and improving robustness.; Les progrès récents de l'apprentissage profond et des réseaux de neurones ont permis d'aborder des tâches complexes de traitement du langage naturel, qui sont appliquées à une pléthore de problèmes réels allant des assistants intelligents dans les appareils mobiles à la prédiction du cancer. Néanmoins, les systèmes modernes basés sur ces approches présentent plusieurs limitations qui peuvent compromettre leurs performances et leur fiabilité, les rendre injustes envers les minorités ou exposer des données personnelles. Nous sommes convaincus que l'intégration de connaissances et de raisonnement symboliques dans le cadre de l'apprentissage profond est une étape nécessaire vers la résolution de ces limitations. Par exemple, les ressources lexicales peuvent enrichir les réseaux de neurones profonds avec des connaissances sémantiques ou syntaxiques, et les règles logiques peuvent fournir des mécanismes d'apprentissage et de raisonnement. Par conséquent, l'objectif de cette thèse est de développer et d'évaluer des moyens d'intégrer différents types de connaissances et de raisonnement symboliques dans un modèle de langage largement utilisé, le Bidirectional Encoder R presentations from Transformers (BERT). Dans un premier temps, nous considérons le retrofitting, une technique simple et populaire pour raffiner les plongements lexicaux de mots grâce à des relations provenant d'un lexique sémantique. Nous présentons deux méthodes inspirées par cette technique pour incorporer ces connaissances dans des plongements contextuels de BERT. Nous évaluons ces méthodes sur trois jeux de données biomédicales pour l'extraction de relations et un jeu de données de critiques de films pour l'analyse des sentiments, et montrons qu'elles n'ont pas d'impact substantiel sur les performances pour ces tâches. En outre, nous effectuons une analyse qualitative afin de mieux comprendre ce résultat négatif. Dans un second temps, nous intégrons le raisonnement analogique à BERT afin d'améliorer ses performances sur la tâche de vérification du sens d'un mot, et de le rendre plus robuste. Pour cela, nous reformulons la vérification du sens d'un mot comme une tâche de détection d'analogie. Nous présentons un modèle hybride qui combine BERT pour encoder les données d'entrée en quadruplets et un classifieur neuronal convolutif pour décider s'ils constituent des analogies valides. Nous testons notre système sur un jeu de données de référence et montrons qu'il peut surpasser les approches existantes. Notre étude empirique montre l'importance de l'encodage d'entrée pour BERT, et comment cette dépendance est atténuée en intégrant les propriétés axiomatiques des analogies lors de l'apprentissage, tout en préservant les performances et en améliorant la robustesse.

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    Authors: Joube, Sylvain; Grasland, Hadrien; Chamont, David; Brunet, Elisabeth;

    Abstract The aim of this work is to compare the performance and ease of programming of the various data transfer strategies provided by SYCL 2020: buffers/accessors on one hand and the different storage types exposed by Unified Shared Memory (USM) on the other hand. We measured the relative performance of USM exclusively located either on the host (USM host) or on the device (USM device), or automatically managed and moved (USM shared). We also tried to quantify the impact of formatting data in a GPU-friendly manner as opposed to using a regular pointer-based structure in which the C++ allocator was replaced by the SYCL allocator sycl::malloc. We first made a PCIe-bound micro-benchmark to test the SYCL memory models with a simple access pattern. We then switched to a real use-case provided by Traccc, a research project associated to the ACTS particle tracking library. The algorithm of interest is SparseCCL, a clustering algorithm used on the first step of track reconstruction. For consistency, all tests were made on a single hardware architecture: a recent server having a discrete GPU with dedicated VRAM, representative of the hardware currently used by the ACTS team.

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    ZENODO; Journal of Physics : Conference Series
    Article . Conference object . 2023 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODO; Journal of P...arrow_drop_down
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      ZENODO; Journal of Physics : Conference Series
      Article . Conference object . 2023 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lopardo, Gianluigi; Precioso, Frédéric; Garreau, Damien;

    In many scenarios, the interpretability of machine learning models is a highly required but difficult task. To explain the individual predictions of such models, local model-agnostic approaches have been proposed. However, the process generating the explanations can be, for a user, as mysterious as the prediction to be explained. Furthermore, interpretability methods frequently lack theoretical guarantees, and their behavior on simple models is frequently unknown. While it is difficult, if not impossible, to ensure that an explainer behaves as expected on a cutting-edge model, we can at least ensure that everything works on simple, already interpretable models. In this paper, we present a theoretical analysis of Anchors (Ribeiro et al., 2018): a popular rule-based interpretability method that highlights a small set of words to explain a text classifier's decision. After formalizing its algorithm and providing useful insights, we demonstrate mathematically that Anchors produces meaningful results when used with linear text classifiers on top of a TF-IDF vectorization. We believe that our analysis framework can aid in the development of new explainability methods based on solid theoretical foundations. Comment: arXiv admin note: substantial text overlap with arXiv:2205.13789

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    https://doi.org/10.48550/arxiv...
    Article . 2023
    License: arXiv Non-Exclusive Distribution
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  • Authors: Hudeček, Vojtěch; Schaub, Léon-Paul; Štancl, Daniel; Paroubek, Patrick; +1 Authors

    International audience; Every model is only as strong as the data that it is trained on. In this paper, we present a new dataset, obtained by merging four publicly available annotated corpora for task-oriented dialogues in several domains (MultiWOZ 2.2, CamRest676, DSTC2 and Schema-Guided Dialogue Dataset). This way, we assess the feasibility of providing a unified ontology and annotation schema covering several domains with a relatively limited effort. We analyze the characteristics of the resulting dataset along three main dimensions: language, information content and performance. We focus on aspects likely to be pertinent for improving dialogue success, e.g. dialogue consistency. Furthermore, to assess the usability of this new corpus, we thoroughly evaluate dialogue generation performance under various conditions with the help of two prominent recent end-to-end dialogue models: MarCo and GPT-2. These models were selected as popular open implementations representative of the two main dimensions of dialogue modelling. While we did not observe a significant gain for dialogue state tracking performance, we show that using more training data from different sources can improve language modelling capabilities and positively impact dialogue flow (consistency). In addition, we provide the community with one of the largest open dataset for machine learning experiments.

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    Authors: Costa, R.; Salgado, A.; Kahn, F.; Ramos, M.; +6 Authors

    International audience; In this paper we will present the research that is taking place at the NOVA CLUNL where an international team is working on a financed project MORDigital. MORDigital's goal is to encode the selected editions of Diccinario de Lingua Portugueza by António de Morais Silva (MOR), first published in 1789.

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    ZENODO
    Presentation . 2022
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    Authors: Almeida, B.; Costa, R.; Salgado, A.; Ramos, M.; +6 Authors

    International audience; This paper describes ongoing work in the modelling of usage information in the context of the MORDigital project. The latter is based on the encoding and publication as linked data of Diccionario da Lingua Portugueza, a Portuguese legacy dictionary authored by António de Morais Silva, whose first edition was published in 1789. In this paper, we focus on the TEI Lex-0 encoding and Ontolex-Lemon modelling of lexicographic articles from the Morais Silva dictionary that feature usage information. The approach described in this paper should be reusable for other projects involving the encoding and linked data publication of legacy dictionaries.

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    Authors: Ahmadi, Sina; Constant, Mathieu; Fort, Karën; Guillaume, Bruno; +1 Authors

    Nous présentons dans ce papier les travaux que nous avons réalisés pour convertir dans le modèle Ontolex-Lemon l'une des plus importantes ressources lexicographiques pour le français : le Trésor de la Langue Française. En effet, malgré l'utilisation généralisée de cette ressource, son format actuel, basé sur XML, ne respecte pas les standards les plus récents de la représentation des données lexicographiques, notamment ceux basés sur les données liées. Nos travaux mettent en lumière la nécessité d'établir des mécanismes permettant d'augmenter l'inter-opérabilité des ressources et des technologies pour créer et maintenir des ressources lexicographiques. In this paper, we report our efforts to convert one of the most comprehensive lexicographic resources of French, the Trésor de la Langue Française, into the Ontolex-Lemon model. Despite the widespread usage of this resource, the original XML format seems to impede its integration in language technology tools. In order to breathe new life into this resource, we examine the usage and the conversion to more interoperable formats, primarily those based on the linguistic linked data, to provide this resource to a broader range of applications and users.

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    Authors: Alsaidi, Safa; Decker, Amandine; Lay, Puthineath; Marquer, Esteban; +2 Authors

    Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them. Comment: Submitted and accepted by the 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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    http://arxiv.org/pdf/2108.0394...
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    https://doi.org/10.1109/dsaa53...
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    Authors: Frank Uiterwaal; Franco Niccolucci; Sheena Bassett; Steven Krauwer; +7 Authors

    This article has been accepted for publication by EUP in the IJHAC: International Journal of Humanities and Arts Computing (https://www.euppublishing.com/loi/ijhac); International audience; Since the first ESFRI roadmap in 2006, multiple humanities Research Infrastructures (RIs) have been set up all over the European continent, supporting archaeologists (ARIADNE), linguists (CLARIN-ERIC), Holocaust researchers (EHRI), cultural heritage specialists (IPERION-CH) and others. These examples only scratch the surface of the breadth of research communities that have benefited from close cooperation in the European Research Area.While each field developed discipline-specific services over the years, common themes can also be distinguished. All humanities RIs address, in varying degrees, questions around research data management, the use of standards and the desired interoperability of data across disciplinary boundaries.This article sheds light on how cluster project PARTHENOS developed pooled services and shared solutions for its audience of humanities researchers, RI managers and policymakers. In a time where the convergence of existing infrastructure is becoming ever more important – with the construction of a European Open Science Cloud as an audacious, ultimate goal – we hope that our experiences inform future work and provide inspiration on how to exploit synergies in interdisciplinary, transnational, scientific cooperation.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cazabet, Rémy; Citraro, Salvatore; Rossetti, Giulio;

    Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this article, we introduce a framework for the generation of random graphs with a controlled size -- number of nodes, edges -- and a customizable structure, beyond blocks and spatial ones, based on node-pair rank and a tunable probability function allowing to control the amount of randomness. We introduce a structure zoo -- a collection of original network structures -- and conduct experiments on the small-world properties of networks generated by those structures. Finally, we introduce an implementation as a Python library named Structify-net. Comment: Accepted version at PCI network science

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  • Authors: Zervakis, Georgios;

    Recent advances in deep learning and neural networks have made it possible to address complex natural language processing tasks, which find application in a plethora of real-world problems ranging from smart assistants in mobile devices to the prediction of cancer. Nonetheless, modern systems based on these frameworks exhibit various limitations that may compromise their performance and trustworthiness, render them unfair towards minorities, or subject them to privacy leakage. It is our belief that integrating symbolic knowledge and reasoning into the deep learning framework is a necessary step towards addressing the aforementioned limitations. For example, lexical resources can enrich deep neural networks with semantic or syntactic knowledge, and logical rules can provide learning and reasoning mechanisms. Therefore, the scope of this thesis is to develop and evaluate ways of integrating different types of symbolic knowledge and reasoning into a widely used language model, Bidirectional Encoder Representations from Transformers (BERT). ln a first stage, we consider retrofitting, a simple and popular technique for refining distributional word embeddings based on relations coming from a semantic lexicon. Inspired by this technique, we present two methods for incorporating this knowledge into BERT contextualized embeddings. We evaluate these methods on three biomedical datasets for relation extraction and one movie review dataset for sentiment analysis, and show that they do not substantially impact the performance for these tasks. Furthermore, we conduct a qualitative analysis to provide further insights on this negative result. ln a second stage, we integrate analogical reasoning with BERT as a means to improve its performance on the target sense verification task, and make it more robust. To do so, we reformulate target sense verification as an analogy detection task. We present a hybrid model that combines BERT to encode the input data into quadruples and a convolutional neural classifier to decide whether they constitute valid analogies. We test our system on a benchmark dataset, and show that it can outperform existing approaches. Our empirical study shows the importance of the input encoding for BERT, and how this dependence gets alleviated by integrating the axiomatic properties of analogies during training, while preserving performance and improving robustness.; Les progrès récents de l'apprentissage profond et des réseaux de neurones ont permis d'aborder des tâches complexes de traitement du langage naturel, qui sont appliquées à une pléthore de problèmes réels allant des assistants intelligents dans les appareils mobiles à la prédiction du cancer. Néanmoins, les systèmes modernes basés sur ces approches présentent plusieurs limitations qui peuvent compromettre leurs performances et leur fiabilité, les rendre injustes envers les minorités ou exposer des données personnelles. Nous sommes convaincus que l'intégration de connaissances et de raisonnement symboliques dans le cadre de l'apprentissage profond est une étape nécessaire vers la résolution de ces limitations. Par exemple, les ressources lexicales peuvent enrichir les réseaux de neurones profonds avec des connaissances sémantiques ou syntaxiques, et les règles logiques peuvent fournir des mécanismes d'apprentissage et de raisonnement. Par conséquent, l'objectif de cette thèse est de développer et d'évaluer des moyens d'intégrer différents types de connaissances et de raisonnement symboliques dans un modèle de langage largement utilisé, le Bidirectional Encoder R presentations from Transformers (BERT). Dans un premier temps, nous considérons le retrofitting, une technique simple et populaire pour raffiner les plongements lexicaux de mots grâce à des relations provenant d'un lexique sémantique. Nous présentons deux méthodes inspirées par cette technique pour incorporer ces connaissances dans des plongements contextuels de BERT. Nous évaluons ces méthodes sur trois jeux de données biomédicales pour l'extraction de relations et un jeu de données de critiques de films pour l'analyse des sentiments, et montrons qu'elles n'ont pas d'impact substantiel sur les performances pour ces tâches. En outre, nous effectuons une analyse qualitative afin de mieux comprendre ce résultat négatif. Dans un second temps, nous intégrons le raisonnement analogique à BERT afin d'améliorer ses performances sur la tâche de vérification du sens d'un mot, et de le rendre plus robuste. Pour cela, nous reformulons la vérification du sens d'un mot comme une tâche de détection d'analogie. Nous présentons un modèle hybride qui combine BERT pour encoder les données d'entrée en quadruplets et un classifieur neuronal convolutif pour décider s'ils constituent des analogies valides. Nous testons notre système sur un jeu de données de référence et montrons qu'il peut surpasser les approches existantes. Notre étude empirique montre l'importance de l'encodage d'entrée pour BERT, et comment cette dépendance est atténuée en intégrant les propriétés axiomatiques des analogies lors de l'apprentissage, tout en préservant les performances et en améliorant la robustesse.

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    Authors: Joube, Sylvain; Grasland, Hadrien; Chamont, David; Brunet, Elisabeth;

    Abstract The aim of this work is to compare the performance and ease of programming of the various data transfer strategies provided by SYCL 2020: buffers/accessors on one hand and the different storage types exposed by Unified Shared Memory (USM) on the other hand. We measured the relative performance of USM exclusively located either on the host (USM host) or on the device (USM device), or automatically managed and moved (USM shared). We also tried to quantify the impact of formatting data in a GPU-friendly manner as opposed to using a regular pointer-based structure in which the C++ allocator was replaced by the SYCL allocator sycl::malloc. We first made a PCIe-bound micro-benchmark to test the SYCL memory models with a simple access pattern. We then switched to a real use-case provided by Traccc, a research project associated to the ACTS particle tracking library. The algorithm of interest is SparseCCL, a clustering algorithm used on the first step of track reconstruction. For consistency, all tests were made on a single hardware architecture: a recent server having a discrete GPU with dedicated VRAM, representative of the hardware currently used by the ACTS team.

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    ZENODO; Journal of Physics : Conference Series
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    Authors: Lopardo, Gianluigi; Precioso, Frédéric; Garreau, Damien;

    In many scenarios, the interpretability of machine learning models is a highly required but difficult task. To explain the individual predictions of such models, local model-agnostic approaches have been proposed. However, the process generating the explanations can be, for a user, as mysterious as the prediction to be explained. Furthermore, interpretability methods frequently lack theoretical guarantees, and their behavior on simple models is frequently unknown. While it is difficult, if not impossible, to ensure that an explainer behaves as expected on a cutting-edge model, we can at least ensure that everything works on simple, already interpretable models. In this paper, we present a theoretical analysis of Anchors (Ribeiro et al., 2018): a popular rule-based interpretability method that highlights a small set of words to explain a text classifier's decision. After formalizing its algorithm and providing useful insights, we demonstrate mathematically that Anchors produces meaningful results when used with linear text classifiers on top of a TF-IDF vectorization. We believe that our analysis framework can aid in the development of new explainability methods based on solid theoretical foundations. Comment: arXiv admin note: substantial text overlap with arXiv:2205.13789

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    https://doi.org/10.48550/arxiv...
    Article . 2023
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  • Authors: Hudeček, Vojtěch; Schaub, Léon-Paul; Štancl, Daniel; Paroubek, Patrick; +1 Authors

    International audience; Every model is only as strong as the data that it is trained on. In this paper, we present a new dataset, obtained by merging four publicly available annotated corpora for task-oriented dialogues in several domains (MultiWOZ 2.2, CamRest676, DSTC2 and Schema-Guided Dialogue Dataset). This way, we assess the feasibility of providing a unified ontology and annotation schema covering several domains with a relatively limited effort. We analyze the characteristics of the resulting dataset along three main dimensions: language, information content and performance. We focus on aspects likely to be pertinent for improving dialogue success, e.g. dialogue consistency. Furthermore, to assess the usability of this new corpus, we thoroughly evaluate dialogue generation performance under various conditions with the help of two prominent recent end-to-end dialogue models: MarCo and GPT-2. These models were selected as popular open implementations representative of the two main dimensions of dialogue modelling. While we did not observe a significant gain for dialogue state tracking performance, we show that using more training data from different sources can improve language modelling capabilities and positively impact dialogue flow (consistency). In addition, we provide the community with one of the largest open dataset for machine learning experiments.

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    Authors: Costa, R.; Salgado, A.; Kahn, F.; Ramos, M.; +6 Authors

    International audience; In this paper we will present the research that is taking place at the NOVA CLUNL where an international team is working on a financed project MORDigital. MORDigital's goal is to encode the selected editions of Diccinario de Lingua Portugueza by António de Morais Silva (MOR), first published in 1789.

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    Authors: Almeida, B.; Costa, R.; Salgado, A.; Ramos, M.; +6 Authors

    International audience; This paper describes ongoing work in the modelling of usage information in the context of the MORDigital project. The latter is based on the encoding and publication as linked data of Diccionario da Lingua Portugueza, a Portuguese legacy dictionary authored by António de Morais Silva, whose first edition was published in 1789. In this paper, we focus on the TEI Lex-0 encoding and Ontolex-Lemon modelling of lexicographic articles from the Morais Silva dictionary that feature usage information. The approach described in this paper should be reusable for other projects involving the encoding and linked data publication of legacy dictionaries.

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    Authors: Ahmadi, Sina; Constant, Mathieu; Fort, Karën; Guillaume, Bruno; +1 Authors

    Nous présentons dans ce papier les travaux que nous avons réalisés pour convertir dans le modèle Ontolex-Lemon l'une des plus importantes ressources lexicographiques pour le français : le Trésor de la Langue Française. En effet, malgré l'utilisation généralisée de cette ressource, son format actuel, basé sur XML, ne respecte pas les standards les plus récents de la représentation des données lexicographiques, notamment ceux basés sur les données liées. Nos travaux mettent en lumière la nécessité d'établir des mécanismes permettant d'augmenter l'inter-opérabilité des ressources et des technologies pour créer et maintenir des ressources lexicographiques. In this paper, we report our efforts to convert one of the most comprehensive lexicographic resources of French, the Trésor de la Langue Française, into the Ontolex-Lemon model. Despite the widespread usage of this resource, the original XML format seems to impede its integration in language technology tools. In order to breathe new life into this resource, we examine the usage and the conversion to more interoperable formats, primarily those based on the linguistic linked data, to provide this resource to a broader range of applications and users.

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    ZENODO
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    Authors: Alsaidi, Safa; Decker, Amandine; Lay, Puthineath; Marquer, Esteban; +2 Authors

    Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them. Comment: Submitted and accepted by the 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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    http://arxiv.org/pdf/2108.0394...
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    https://doi.org/10.1109/dsaa53...
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    https://doi.org/10.48550/arxiv...
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    Authors: Frank Uiterwaal; Franco Niccolucci; Sheena Bassett; Steven Krauwer; +7 Authors

    This article has been accepted for publication by EUP in the IJHAC: International Journal of Humanities and Arts Computing (https://www.euppublishing.com/loi/ijhac); International audience; Since the first ESFRI roadmap in 2006, multiple humanities Research Infrastructures (RIs) have been set up all over the European continent, supporting archaeologists (ARIADNE), linguists (CLARIN-ERIC), Holocaust researchers (EHRI), cultural heritage specialists (IPERION-CH) and others. These examples only scratch the surface of the breadth of research communities that have benefited from close cooperation in the European Research Area.While each field developed discipline-specific services over the years, common themes can also be distinguished. All humanities RIs address, in varying degrees, questions around research data management, the use of standards and the desired interoperability of data across disciplinary boundaries.This article sheds light on how cluster project PARTHENOS developed pooled services and shared solutions for its audience of humanities researchers, RI managers and policymakers. In a time where the convergence of existing infrastructure is becoming ever more important – with the construction of a European Open Science Cloud as an audacious, ultimate goal – we hope that our experiences inform future work and provide inspiration on how to exploit synergies in interdisciplinary, transnational, scientific cooperation.

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    International Journal of Humanities and Arts Computing
    Article . 2021 . Peer-reviewed
    License: EUP TDM
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ International Journa...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      International Journal of Humanities and Arts Computing
      Article . 2021 . Peer-reviewed
      License: EUP TDM
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ISTI Open Portal
      Article . 2021
      Data sources: ISTI Open Portal
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      CNR ExploRA
      Article . 2021
      Data sources: CNR ExploRA
      Hal-Diderot
      Article . 2021
      License: CC BY
      Data sources: Hal-Diderot
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

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