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 • 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.
no code implementations • 26 Dec 2018 • Milan Dojchinovski, Julio Hernandez, Markus Ackermann, Amit Kirschenbaum, Sebastian Hellmann
The aim of the dataset is two-fold: to dramatically broaden and deepen the amount of structured information in DBpedia, and to provide large-scale and multilingual language resource for development of various NLP and IR task.
no code implementations • LREC 2016 • Milan Dojchinovski, Felix Sasaki, Tatjana Gornostaja, Sebastian Hellmann, Erik Mannens, Frank Salliau, Michele Osella, Phil Ritchie, Giannis Stoitsis, Kevin Koidl, Markus Ackermann, Nilesh Chakraborty
In the recent years, Linked Data and Language Technology solutions gained popularity.
no code implementations • LREC 2016 • Martin Br{\"u}mmer, Milan Dojchinovski, Sebastian Hellmann
The ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora.
no code implementations • LREC 2016 • Milan Dojchinovski, Dinesh Reddy, Tom{\'a}{\v{s}} Kliegr, Tom{\'a}{\v{s}} Vitvar, Harald Sack
In this paper, we present a crowdsourced dataset which adds entity salience (importance) annotations to the Reuters-128 dataset, which is subset of Reuters-21578.