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Found 8 papers, 2 papers with code
Assessing Wordnets with WordNet Embeddings
An effective conversion method was proposed in the literature to obtain a lexical semantic space from a lexical semantic graph, thus permitting to obtain WordNet embeddings from WordNets.
PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguese
Similarly, the respective fine-tuned neural language models, developed with a low-rank adaptation approach, are made available as baselines that can stimulate future work on the neural processing of Portuguese.
Fostering the Ecosystem of Open Neural Encoders for Portuguese with Albertina PT* Family
To foster the neural encoding of Portuguese, this paper contributes foundation encoder models that represent an expansion of the still very scarce ecosystem of large language models specifically developed for this language that are fully open, in the sense that they are open source and openly distributed for free under an open license for any purpose, thus including research and commercial usages.
Advancing Generative AI for Portuguese with Open Decoder Gervásio PT*
To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect.
Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*
To advance the neural encoding of Portuguese (PT), and a fortiori the technological preparation of this language for the digital age, we developed a Transformer-based foundation model that sets a new state of the art in this respect for two of its variants, namely European Portuguese from Portugal (PT-PT) and American Portuguese from Brazil (PT-BR).
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification
Experimental results show that transfer learning techniques are beneficial to the task and that current methods may be insufficient to leverage commonsense knowledge from different lexical semantic families.
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness
Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a vector space, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately?
Biosignals learning and synthesis using deep neural networks
Background Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals.
