Deep Learning Based Multi-Label Text Classification of UNGA Resolutions

The main goal of this research is to produce a useful software for United Nations (UN), that could help to speed up the process of qualifying the UN documents following the Sustainable Development Goals (SDGs) in order to monitor the progresses at the world level to fight poverty, discrimination, climate changes. In fact human labeling of UN documents would be a daunting task given the size of the impacted corpus. Thus, automatic labeling must be adopted at least as a first step of a multi-phase process to reduce the overall effort of cataloguing and classifying. Deep Learning (DL) is nowadays one of the most powerful tools for state-of-the-art (SOTA) AI for this task, but very often it comes with the cost of an expensive and error-prone preparation of a training-set. In the case of multi-label text classification of domain-specific text it seems that we cannot effectively adopt DL without a big-enough domain-specific training-set. In this paper, we show that this is not always true. In fact we propose a novel method that is able, through statistics like TF-IDF, to exploit pre-trained SOTA DL models (such as the Universal Sentence Encoder) without any need for traditional transfer learning or any other expensive training procedure. We show the effectiveness of our method in a legal context, by classifying UN Resolutions according to their most related SDGs.Comment: 10 pages, 10 figures, accepted paper at ICEGOV 202

Réglage fin par ensemble convergent de divers modèles transformers pour l’analyse du sentiment

Le développement des techniques de réglage fin pour les modèles transformers pré-entraînés comme BERT (Devlin et al., 2018 [22]) a beaucoup gagné en popularité depuis 2018. Comparativement à la technique d’extraction des caractéristiques, le réglage fin donne des résultats de pointe sur de nombreuses tâches en aval liées au traitement automatique du langage naturel (Ma et al., 2019 [65]). En analyse du sentiment, ces avancées aident à surmonter plusieurs enjeux techniques importants, comme la gestion des termes de négation, des anaphores et des références syntagmatiques plus subtiles (Birjali et al., 2021 [10]). Néanmoins, certains enjeux demeurent nonrésolus, notamment la difficulté d’obtenir des données d’entraînement pour des tâches-domaines spécialisées (Xu et al., 2019 [113]). Cette problématique est liée au coût et à la difficulté de créer des jeux de données manuellement annotés par des êtres humains. Dans ce mémoire, une nouvelle solution d’annotation programmatique est proposée, s’appuyant sur les travaux de Ghashiya et Okamura (2021) [34] avec des modèles lexicaux en analyse du sentiment portant sur les titres d’actualité durant la Covid-19. Comme alternative aux modèles lexicaux, la technique proposée utilise un modèle de réglage fin par ensemble de modèles transformers appelé MDLsoft. Cette technique combine des aspects de deux stratégies de réglage fin, soit les auto-ensembles par votes (Xu et al., 2020 [115]) et l’apprentissage multitâches (Liu et al., 2019 [62]). À partir des prédictions issues du modèle final, une analyse du sentiment sera présentée portant sur le même sujet que Ghashiya et Okamura, mais en y apportant certaines améliorations techniques

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Sustainable Construction Engineering and Management

This Book is a Printed Edition of the Special Issue which covers sustainability as an emerging requirement in the fields of construction management, project management and engineering. We invited authors to submit their theoretical or experimental research articles that address the challenges and opportunities for sustainable construction in all its facets, including technical topics and specific operational or procedural solutions, as well as strategic approaches aimed at the project, company or industry level. Central to developments are smart technologies and sophisticated decision-making mechanisms that augment sustainable outcomes. The Special Issue was received with great interest by the research community and attracted a high number of submissions. The selection process sought to balance the inclusion of a broad representative spread of topics against research quality, with editors and reviewers settling on thirty-three articles for publication. The Editors invite all participating researchers and those interested in sustainable construction engineering and management to read the summary of the Special Issue and of course to access the full-text articles provided in the Book for deeper analyses