Mariano Rico


2020

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Recent Developments for the Linguistic Linked Open Data Infrastructure
Thierry Declerck | John Philip McCrae | Matthias Hartung | Jorge Gracia | Christian Chiarcos | Elena Montiel-Ponsoda | Philipp Cimiano | Artem Revenko | Roser Saurí | Deirdre Lee | Stefania Racioppa | Jamal Abdul Nasir | Matthias Orlikowsk | Marta Lanau-Coronas | Christian Fäth | Mariano Rico | Mohammad Fazleh Elahi | Maria Khvalchik | Meritxell Gonzalez | Katharine Cooney
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper we describe the contributions made by the European H2020 project “Prêt-à-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. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

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Hypernym-LIBre: A Free Web-based Corpus for Hypernym Detection
Shaurya Rawat | Mariano Rico | Oscar Corcho
Proceedings of the 12th Web as Corpus Workshop

In this paper, we describe a new web-based corpus for hypernym detection. It consists of 32 GB of high quality english paragraphs along with their part-of-speech tagged and dependency parsed versions. For hypernym detection, the current state-of-the-art uses a corpus which is not available freely. We evaluate the state-of-the-art methods on our corpus and achieve similar results. The advantage of this corpora is that it is available under an open license. Our main contribution is the corpus with POS-tags and dependency tags and the code to extract and simulate the results we have achieved using our corpus.