Zobrazeno 1 - 10
of 507
pro vyhledávání: '"A. Labaka"'
Publikováno v:
XX Conferencia de la Asociacion Espanola para la Inteligencia Artificial 2024
Climate change-associated disasters have become a significant concern, principally when affecting urban areas. Assessing these regions' resilience to strengthen their disaster management is crucial, especially in the areas vulnerable to windstorms, o
Externí odkaz:
http://arxiv.org/abs/2411.14439
Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision. In this paper, we formalize the implicit similarity function induced by this approach, and show tha
Externí odkaz:
http://arxiv.org/abs/2205.12213
Publikováno v:
Heliyon, Vol 10, Iss 12, Pp e33116- (2024)
Decision Support Systems (DSS) have emerged as important tools for enhancing community resilience due to their ability to provide timely and efficient solutions to disaster-related problems while reflecting the perspectives of different stakeholders
Externí odkaz:
https://doaj.org/article/06d0338aad15474ba4467a5eef776ea8
Publikováno v:
Progress in Disaster Science, Vol 22, Iss , Pp 100320- (2024)
The escalating impact of disasters underscores the urgency of building resilient communities. Interactions among community stakeholders play a pivotal role in fostering resilience but improving such interactions is often hindered by competing priorit
Externí odkaz:
https://doaj.org/article/f42e02079f4f4cceb1fa49676cadce49
Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 mi
Externí odkaz:
http://arxiv.org/abs/2109.03659
Publikováno v:
In Heliyon 30 June 2024 10(12)
Publikováno v:
In Progress in Disaster Science April 2024 22
Publikováno v:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics - Student Research Workshop, pages 255-262, Association for Computational Linguistics, 2020
Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingua
Externí odkaz:
http://arxiv.org/abs/2105.10419
Publikováno v:
In International Journal of Disaster Risk Reduction 1 February 2024 101
Autor:
Carlos Yánez Benítez, Teófilo Lorente-Aznar, Idurre Labaka, Marcelo A. F. Ribeiro Jr, Yosu Viteri, Koji Morishita, Marta Baselga, Antonio Güemes
Publikováno v:
BMC Emergency Medicine, Vol 23, Iss 1, Pp 1-8 (2023)
Abstract Background Our study aimed to assess the ability of nonmedical civilians to self-apply extremity tourniquets in cold weather conditions while wearing insulating technical clothing after receiving basic training. Methods A field study was con
Externí odkaz:
https://doaj.org/article/cf5678bd132c41b88c821dd860aa25c4