CARDELLINO , Cristian

In this abstract we present a methodology to improve Argument annotation guidelines by exploiting inter-annotator agreement measures.After a first stage of the annotation effort, we have detected problematic issues via an analysis of inter-annotator agreement. Wehave detected ill-defined concepts, which we have addressed by redefining...

In this paper, we present an ontology population approach for legal ontologies. We exploit Wikipedia as a source of manually annotated examples of legal entities. We align YAGO, a Wikipedia-based ontology, and LKIF, an ontology specifically designed for the legal domain. Through this alignment, we can effectively populate the LKIF ontology,...

In this paper, we present a Wikipedia-based approach to develop resources for the legal domain. We establish a mapping between a legal domain ontology, LKIF (Hoekstra et al., 2007), and a Wikipedia-based ontology, YAGO (Suchanek et al., 2007), and through that we populate LKIF. Moreover, we use the mentions of those entities in Wikipedia text...

Managing licensing information and data rights is becoming a crucial issue in the Linked (Open) Data scenario. An open problem in this scenario is how to associate machine-readable licenses specifications to the data, so that automated approaches to treat such information can be fruitfully exploited to avoid data misuse. This means that we need...

In this paper, we try to improve Information Extraction in legal texts by creating a legal Named Entity Recognizer, Classifier and Linker. With this tool, we can identify relevant parts of texts and connect them to a structured knowledge representation, the LKIF ontology.More interestingly, this tool has been developed with relatively little...

Active learning has been successfully applied to a number of NLP tasks. In this paper, we present a study on Information Extraction for natural language licenses that need to be translated to RDF. The final purpose of our work is to automatically extract from a natural language document specifying a certain license a machine-readable...

International audience

We present a methodology to enhance domain-specific ontologies by (i) manual annotation of texts with the concepts in the domain ontology, (ii) matching annotated concepts with the closest YAGO-Wikipedia concept and (iii) using concepts from other ontologies that cover complementary domains. This method reduces the difficulty of aligning...

In this paper we adapt the semi-supervised deep learning architecture known as "Convolutional Ladder Networks", from the domain of computer vision, and explore how well it works for a semi-supervised Named Entity Recognition and Classification task with legal data. The idea of exploring a semi-supervised technique is to assess the impact of...

We show that the structure of communities in social me- dia provides robust information for weakly supervised approaches to assign stances to tweets. Using as seed the SemEval 2016 Stance Detection Task annotated data, we retrieved a high number of topically related tweets. We then propagated information from the manually an- notated seed to...

International audience

We present a methodology to enhance domain-specific ontologies by (i) addressing a manual annotation of texts with the concepts in the domain ontology, (ii) matching the annotated concepts with the closest YAGO-Wikipedia concept available, and (iii) using concepts from other ontologies that cover complementary domains. This method reduces the...

The need for transparent AI systems in sensitive domains like medicine has become key. In this paper we present ANTIDOTE, a software suite proposing different tools for argumentation-driven explainable Artificial Intelligence for digital medicine. Our system offers the following functionalities: multilingual argumentative analysis for the...