Science, technology and innovation (STI) policies have evolved in the past decade. We are now progressing towards policies that are more aligned with sustainable development through integrating social, economic and environmental dimensions. In this new policy environment, the need to keep track of innovation from its conception in Science and Research has emerged. Argumentation mining, an interdisciplinary NLP field, gives rise to the required technologies. In this study, we present the first STI-driven multidisciplinary corpus of scientific abstracts annotated for argumentative units (AUs) on the sustainable development goals (SDGs) set by the United Nations (UN). AUs are the sentences conveying the Claim(s) reported in the author’s original research and the Evidence provided for support. We also present a set of strong, BERT-based neural baselines achieving an f1-score of 70.0 for Claim and 62.4 for Evidence identification evaluated with 10-fold cross-validation. To demonstrate the effectiveness of our models, we experiment with different test sets showing comparable performance across various SDG policy domains. Our dataset and models are publicly available for research purposes.

The advent of Big Data has shifted social science research towards computational methods. The volume of data that is nowadays available has brought a radical change in traditional approaches due to the cost and effort needed for processing. Knowledge extraction from heterogeneous and ample data is not an easy task to tackle. Thus, interdisciplinary approaches are necessary, combining experts of both social and computer science. This paper aims to present a work in the context of protest analysis, which falls into the scope of Computational Social Science. More specifically, the contribution of this work is to describe a Computational Social Science methodology for Event Analysis. The presented methodology is generic in the sense that it can be applied in every event typology and moreover, it is innovative and suitable for interdisciplinary tasks as it incorporates the human-in-the-loop. Additionally, a case study is presented concerning Protest Analysis in Greece over the last two decades. The conceptual foundation lies mainly upon claims analysis, and newspaper data were used in order to map, document and discuss protests in Greece in a longitudinal perspective.

Cat. 2 Show-case: We present the Data4Impact (D4I) platform, a novel end-to-end system for evidence-based, timely and accurate monitoring and evaluation of research and innovation (R&I) activities. Using the latest technological advances in Human Language Technology (HLT) and our data-driven methodology, we build a novel set of indicators in order to track funded projects and their impact on science, the economy and the society as a whole, during and after the project life-cycle. We develop our methodology by targeting Health-related EC projects from 2007 to 2019 to produce solutions that meet the needs of stakeholders (mainly policy-makers and research funders). Various D4I text analytics workflows process datasets and their metadata, extract valuable insights and estimate intermediate results and metrics, culminating in a set of robust indicators that the users can interact with through our dashboard, the D4I Monitor (available at monitor.data4impact.eu). Therefore, our approach, which can be generalized to different contexts, is multidimensional (technology, tools, indicators, dashboard) and the resulting system can provide an innovative solution for public administrators in their policy-making needs related to RDI funding allocation.

In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter. Specifically the proposed system participated both to tweet polarity classification (two-, three- and five class) and tweet quantification (two and five-class) tasks.

This paper presents META-SHARE (www.meta-share.eu), an open language resource infrastructure, and its usage since its Europe-wide deployment in early 2013. META-SHARE is a network of repositories that store language resources (data, tools and processing services) documented with high-quality metadata, aggregated in central inventories allowing for uniform search and access. META-SHARE was developed by META-NET (www.meta-net.eu) and aims to serve as an important component of a language technology marketplace for researchers, developers, professionals and industrial players, catering for the full development cycle of language technology, from research through to innovative products and services. The observed usage in its initial steps, the steadily increasing number of network nodes, resources, users, queries, views and downloads are all encouraging and considered as supportive of the choices made so far. In tandem, take-up activities like direct linking and processing of datasets by language processing services as well as metadata transformation to RDF are expected to open new avenues for data and resources linking and boost the organic growth of the infrastructure while facilitating language technology deployment by much wider research communities and industrial sectors.

This paper presents a metadata model for the description of language resources proposed in the framework of the META-SHARE infrastructure, aiming to cover both datasets and tools/technologies used for their processing. It places the model in the overall framework of metadata models, describes the basic principles and features of the model, elaborates on the distinction between minimal and maximal versions thereof, briefly presents the integrated environment supporting the LRs description and search and retrieval processes and concludes with work to be done in the future for the improvement of the model.

The paper reports on the development methodology of a system aimed at multi-domain multi-lingual recognition and classification of names in texts, the focus being on the linguistic resources used for training and testing purposes. The corpus presented here has been collected and annotated in the framework of different projects the critical issue being the development of a final resource that is homogenous, re-usable and adaptable to different domains and languages with a view to robust multi-domain and multi-lingual NERC.

In this paper we give an overview of the approach adopted to add a layer of semantic information to the Greek Dependency Treebank [GDT]. Our ultimate goal is to come up with a large corpus, reliably annotated with rich semantic structures. To this end, a corpus has been compiled encompassing various data sources and domains. This collection has been preprocessed, annotated and validated on the basis of dependency representation. Taking into account multi-layered annotation schemes designed to provide deeper representations of structure and meaning, we describe the methodology followed as regards the semantic layer, we report on the annotation process and the problems faced and we conclude with comments on future work and exploitation of the resulting resource.