Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Santus Enrico"'
Autor:
Winata, Genta Indra, Hudi, Frederikus, Irawan, Patrick Amadeus, Anugraha, David, Putri, Rifki Afina, Wang, Yutong, Nohejl, Adam, Prathama, Ubaidillah Ariq, Ousidhoum, Nedjma, Amriani, Afifa, Rzayev, Anar, Das, Anirban, Pramodya, Ashmari, Adila, Aulia, Wilie, Bryan, Mawalim, Candy Olivia, Cheng, Ching Lam, Abolade, Daud, Chersoni, Emmanuele, Santus, Enrico, Ikhwantri, Fariz, Kuwanto, Garry, Zhao, Hanyang, Wibowo, Haryo Akbarianto, Lovenia, Holy, Cruz, Jan Christian Blaise, Putra, Jan Wira Gotama, Myung, Junho, Susanto, Lucky, Machin, Maria Angelica Riera, Zhukova, Marina, Anugraha, Michael, Adilazuarda, Muhammad Farid, Santosa, Natasha, Limkonchotiwat, Peerat, Dabre, Raj, Audino, Rio Alexander, Cahyawijaya, Samuel, Zhang, Shi-Xiong, Salim, Stephanie Yulia, Zhou, Yi, Gui, Yinxuan, Adelani, David Ifeoluwa, Lee, En-Shiun Annie, Okada, Shogo, Purwarianti, Ayu, Aji, Alham Fikri, Watanabe, Taro, Wijaya, Derry Tanti, Oh, Alice, Ngo, Chong-Wah
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a mas
Externí odkaz:
http://arxiv.org/abs/2410.12705
Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Language Processing community using large pre-trained language models, such a
Externí odkaz:
http://arxiv.org/abs/2306.05276
Autor:
Portelli, Beatrice, Scaboro, Simone, Santus, Enrico, Sedghamiz, Hooman, Chersoni, Emmanuele, Serra, Giuseppe
Medical term normalization consists in mapping a piece of text to a large number of output classes. Given the small size of the annotated datasets and the extremely long tail distribution of the concepts, it is of utmost importance to develop models
Externí odkaz:
http://arxiv.org/abs/2210.11947
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackling Tasks 1, 2, 5, 6 and 10. T
Externí odkaz:
http://arxiv.org/abs/2209.03452
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use Natural Language P
Externí odkaz:
http://arxiv.org/abs/2209.02812
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is curren
Externí odkaz:
http://arxiv.org/abs/2109.10080
While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised alternative to Masked Language Modeling (MLM) for improving sequence rep
Externí odkaz:
http://arxiv.org/abs/2109.07424
Autor:
Raval, Shivam, Sedghamiz, Hooman, Santus, Enrico, Alhanai, Tuka, Ghassemi, Mohammad, Chersoni, Emmanuele
Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes it logistically intractable to analyze using human agents, with NLP rep
Externí odkaz:
http://arxiv.org/abs/2109.05815
Autor:
Pedinotti, Paolo, Rambelli, Giulia, Chersoni, Emmanuele, Santus, Enrico, Lenci, Alessandro, Blache, Philippe
Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we tak
Externí odkaz:
http://arxiv.org/abs/2107.10922
Autor:
Portelli, Beatrice, Passabì, Daniele, Lenzi, Edoardo, Serra, Giuseppe, Santus, Enrico, Chersoni, Emmanuele
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeking to resort to NLP to monitor these outlets. We propose for the first
Externí odkaz:
http://arxiv.org/abs/2105.08882