The use of social media in EU policy communication and implications for the emergence of a European public sphere

Cohesion policy is the European Union’s (EU) main investment policy and seeks to strengthen economic, social and territorial cohesion. While accomplishments in this regard are constantly measured, European citizens are not always aware of the policy’s impact and the role the EU plays therein. This is especially relevant as the communication of EU policies is central to the emergence of a European public sphere, an acknowledged condition for European integration. In this paper, we aim at advancing research in this regard through the analysis of cohesion policy communication on the social media channels of ten Local Managing Authorities (LMAs) responsible for managing and communicating structural funds at the local level. By building on a bottom-up construction of shared meaning structures through semi-automatic analysis techniques, we make the following three observations: first, social media communication is indicative of "horizontal Europeanization"; second, Europeanization occurs both in the form of the spontaneous amalgamation of shared discontent expressed by citizens and the institutionalization of top-down EU communication measures adopted by LMAs; and third, a cluster of topics articulated internationally and showcasing a negative attitude towards the EU funding scheme suggests that, counter-intuitively, Euroscepticism seems to facilitate the emergence of a European public sphere

Rivière or Fleuve? Modelling Multilinguality in the Hydrographical

The need for interoperability among geospatial resources in different natural languages evidences the difficulties to cope with domain representations highly dependent of the culture in which they have been conceived. In this paper we characterize the problem of representing cultural discrepancies in ontologies. We argue that such differences can be accounted for at the ontology terminological layer by means of external elaborated models of linguistic information associated to ontologies. With the aim of showing how external models can cater for cultural discrepancies, we compare two versions of an ontology of the hydrographical domain: hydrOntology. The first version makes use of the labeling system supported by RDF(S) and OWL to include multilingual linguistic information in the ontology. The second version relies on the Linguistic Information Repository model (LIR) to associate structured multilingual information to ontology concepts. In this paper we propose an extension to the LIR to better capture linguistic and cultural specificities within and across language

The construction of a linguistic linked data framework for bilingual lexicographic resources

Little-known lexicographic resources can be of tremendous value to users once digitised. By extending the digitisation efforts for a lexicographic resource, converting the human readable digital object to a state that is also machine-readable, structured data can be created that is semantically interoperable, thereby enabling the lexicographic resource to access, and be accessed by, other semantically interoperable resources. The purpose of this study is to formulate a process when converting a lexicographic resource in print form to a machine-readable bilingual lexicographic resource applying linguistic linked data principles, using the English-Xhosa Dictionary for Nurses as a case study. This is accomplished by creating a linked data framework, in which data are expressed in the form of RDF triples and URIs, in a manner which allows for extensibility to a multilingual resource. Click languages with characters not typically represented by the Roman alphabet are also considered. The purpose of this linked data framework is to define each lexical entry as “historically dynamic”, instead of “ontologically static” (Rafferty, 2016:5). For a framework which has instances in constant evolution, focus is thus given to the management of provenance and linked data generation thereof. The output is an implementation framework which provides methodological guidelines for similar language resources in the interdisciplinary field of Library and Information Science

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Bibliographie sur l’accès aux études supérieures, les parcours d’études et l’insertion professionnelle dans la perspective du développement culturel, économique et social de la région montréalaise: 3e partie

3e parti

Sentiment Analysis for the Low-Resourced Latinised Arabic "Arabizi"

The expansion of digital communication mediums from private mobile messaging into the public through social media presented an opportunity for the data science research and industry to mine the generated big data for artificial information extraction. A popular information extraction task is sentiment analysis, which aims at extracting polarity opinions, positive, negative, or neutral, from the written natural language. This science helped organisations better understand the public’s opinion towards events, news, public figures, and products. However, sentiment analysis has advanced for the English language ahead of Arabic. While sentiment analysis for Arabic is developing in the literature of Natural Language Processing (NLP), a popular variety of Arabic, Arabizi, has been overlooked for sentiment analysis advancements. Arabizi is an informal transcription of the spoken dialectal Arabic in Latin script used for social texting. It is known to be common among the Arab youth, yet it is overlooked in efforts on Arabic sentiment analysis for its linguistic complexities. As to Arabic, Arabizi is rich in inflectional morphology, but also codeswitched with English or French, and distinctively transcribed without adhering to a standard orthography. The rich morphology, inconsistent orthography, and codeswitching challenges are compounded together to have a multiplied effect on the lexical sparsity of the language, where each Arabizi word becomes eligible to be spelled in many ways, that, in addition to the mixing of other languages within the same textual context. The resulting high degree of lexical sparsity defies the very basics of sentiment analysis, classification of positive and negative words. Arabizi is even faced with a severe shortage of data resources that are required to set out any sentiment analysis approach. In this thesis, we tackle this gap by conducting research on sentiment analysis for Arabizi. We addressed the sparsity challenge by harvesting Arabizi data from multi-lingual social media text using deep learning to build Arabizi resources for sentiment analysis. We developed six new morphologically and orthographically rich Arabizi sentiment lexicons and set the baseline for Arabizi sentiment analysis on social media