TERUEL , Milagro

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...

Argument mining is a rising area of Natural Language Processing (NLP) concerned with the automatic recognition and interpretation of argument components and their relations. Neural models are by now mature technologies to be exploited for automating the argument mining tasks, despite the issue of data sparseness. This could ease much of the...

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...

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 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 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 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...