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pro vyhledávání: '"D'Alleva A."'
Autor:
Alleva, Eugenia, Landi, Isotta, Shaw, Leslee J, Böttinger, Erwin, Fuchs, Thomas J, Ensari, Ipek
Clinical note classification is a common clinical NLP task. However, annotated data-sets are scarse. Prompt-based learning has recently emerged as an effective method to adapt pre-trained models for text classification using only few training example
Externí odkaz:
http://arxiv.org/abs/2310.20089
Autor:
Landi, Isotta, Alleva, Eugenia, Valentine, Alissa A., Lepow, Lauren A., Charney, Alexander W.
Despite being a unique source of information on patients' status and disease progression, clinical notes are characterized by high levels of duplication and information redundancy. In general domain text, it has been shown that deduplication does not
Externí odkaz:
http://arxiv.org/abs/2312.09469
Autor:
Alleva, Giorgio, Arbia, Giuseppe, Falorsi, Piero Demetrio, Nardelli, Vincenzo, Zuliani, Alberto
The pandemic linked to COVID-19 infection represents an unprecedented clinical and healthcare challenge for many medical researchers attempting to prevent its worldwide spread. This pandemic also represents a major challenge for statisticians involve
Externí odkaz:
http://arxiv.org/abs/2103.01254
Autor:
Alleva, Giorgio, Arbia, Giuseppe, Falorsi, Piero Demetrio, Nardelli, Vincenzo, Zuliani, Alberto
Given the urgent informational needs connected with the diffusion of infection with regard to the COVID-19 pandemic, in this paper, we propose a sampling design for building a continuous-time surveillance system. Compared with other observational str
Externí odkaz:
http://arxiv.org/abs/2004.06068
Publikováno v:
Proc. Interspeech 2018, 3038-3042
The goal of this work is to develop a meeting transcription system that can recognize speech even when utterances of different speakers are overlapped. While speech overlaps have been regarded as a major obstacle in accurately transcribing meetings,
Externí odkaz:
http://arxiv.org/abs/1810.03655
Publikováno v:
Proc. IEEE ICASSP, April 2018, pp. 5934-5938
We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Sw
Externí odkaz:
http://arxiv.org/abs/1708.06073