OpenAIRE: Envíos recentes

Mostrando ítems 1-5 de 287

    • Wind-solar technological, spatial and temporal complementarities in Europe: A portfolio approach 

      López Prol, Javier; DeLlano-Paz, Fernando; Calvo-Silvosa, Anxo; Pfenninger, Stefan; Staffell, Iain (Elsevier, 2024)
      [Abstract]: Climate change and geopolitical risks call for the rapid transformation of electricity systems worldwide, with Europe at the forefront. Wind and solar are the lowest cost, lowest risk, and cleanest energy ...

    • A Transition-Based Algorithm for Unrestricted AMR Parsing 

      Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics, 2018-06)
      [Absctract]: Non-projective parsing can be useful to handle cycles and reentrancy in AMR graphs. We explore this idea and introduce a greedy left-to-right non-projective transition-based parser. At each parsing configuration, ...

    • Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter 

      Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics, 2018-06)
      [Absctract]: The usage of part-of-day nouns, such as ‘night’, and their time-specific greetings (‘good night’), varies across languages and cultures. We show the possibilities that Twitter offers for studying the semantics ...

    • Harry Potter and the Action Prediction Challenge from Natural Language 

      Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics, 2019-06)
      [Absctract]: We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of study, we consider the ...

    • Sequence Labeling Parsing by Learning across Representations 

      Strzyz, Michalina; Vilares, David; Gómez-Rodríguez, Carlos (Association for Computational Linguistics, 2019-07)
      [Absctract]: We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that ...