1 code implementation • LREC 2016 • Marta Villegas, Maite Melero, N{\'u}ria Bel, Jorge Gracia
The experiments presented here exploit the properties of the Apertium RDF Graph, principally cycle density and nodes{'} degree, to automatically generate new translation relations between words, and therefore to enrich existing bilingual dictionaries with new entries.
no code implementations • LREC 2014 • Jorge Gracia, Elena Montiel-Ponsoda, Daniel Vila-Suero, Guadalupe Aguado-de-Cea
As a proof of concept, we have extracted the translations of the terms contained in Terminesp, a multilingual terminological database, and represented them as linked data.
no code implementations • WS 2019 • Georg Rehm, Juli{\'a}n Moreno-Schneider, Jorge Gracia, Artem Revenko, Victor Mireles, Maria Khvalchik, Ilan Kernerman, Andis Lagzdins, Marcis Pinnis, Artus Vasilevskis, Elena Leitner, Jan Milde, Pia Wei{\ss}enhorn
We present a portfolio of natural legal language processing and document curation services currently under development in a collaborative European project.
no code implementations • LREC 2016 • John Philip McCrae, Christian Chiarcos, Francis Bond, Philipp Cimiano, Thierry Declerck, Gerard de Melo, Jorge Gracia, Sebastian Hellmann, Bettina Klimek, Steven Moran, Petya Osenova, Antonio Pareja-Lora, Jonathan Pool
The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections.
no code implementations • 25 Feb 2020 • María G. Buey, Carlos Bobed, Jorge Gracia, Eduardo Mena
In this paper, we propose an approach to keyword disambiguation which grounds on a semantic relatedness between words and senses provided by an external inventory (ontology) that is not known at training time.
no code implementations • LREC 2020 • Julián Moreno-Schneider, Georg Rehm, Elena Montiel-Ponsoda, Víctor Rodriguez-Doncel, Artem Revenko, Sotirios Karampatakis, Maria Khvalchik, Christian Sageder, Jorge Gracia, Filippo Maganza
Legal technology is currently receiving a lot of attention from various angles.
no code implementations • LREC 2020 • Marta Lanau-Coronas, Jorge Gracia
This paper describes the participation of two different approaches in the 3rd Translation Inference Across Dictionaries (TIAD 2020) shared task.
no code implementations • LREC 2020 • Thierry Declerck, John Philip McCrae, Matthias Hartung, Jorge Gracia, Christian Chiarcos, Elena Montiel-Ponsoda, Philipp Cimiano, Artem Revenko, Roser Saur{\'\i}, Deirdre Lee, Stefania Racioppa, Jamal Abdul Nasir, Matthias Orlikowsk, Marta Lanau-Coronas, Christian F{\"a}th, Mariano Rico, Mohammad Fazleh Elahi, Maria Khvalchik, Meritxell Gonzalez, Katharine Cooney
In this paper we describe the contributions made by the European H2020 project {``}Pr{\^e}t-{\`a}-LLOD{''} ({`}Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors{'}) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure.
no code implementations • gwll (LREC) 2022 • Jorge Gracia, Besim Kabashi, Ilan Kernerman
The objective of the Translation Inference Across Dictionaries (TIAD) series of shared tasks is to explore and compare methods and techniques that infer translations indirectly between language pairs, based on other bilingual/multilingual lexicographic resources.
no code implementations • LREC 2022 • Michael Rosner, Sina Ahmadi, Elena-Simona Apostol, Julia Bosque-Gil, Christian Chiarcos, Milan Dojchinovski, Katerina Gkirtzou, Jorge Gracia, Dagmar Gromann, Chaya Liebeskind, Giedrė Valūnaitė Oleškevičienė, Gilles Sérasset, Ciprian-Octavian Truică
In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages.
no code implementations • LDL (ACL) 2022 • Fernando Bobillo, Julia Bosque-Gil, Jorge Gracia, Marta Lanau-Coronas
The OntoLex-Lemon model provides a vocabulary to enrich ontologies with linguistic information that can be exploited by Natural Language Processing applications.
no code implementations • LDL (ACL) 2022 • Fahad Khan, Christian Chiarcos, Thierry Declerck, Maria Pia di Buono, Milan Dojchinovski, Jorge Gracia, Giedre Valunaite Oleskeviciene, Daniela Gifu
This article discusses a survey carried out within the NexusLinguarum COST Action which aimed to give an overview of existing guidelines (GLs) and best practices (BPs) in linguistic linked data.