Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ai, Xuguang"'
Autor:
Gilson, Aidan, Ai, Xuguang, Arunachalam, Thilaka, Chen, Ziyou, Cheong, Ki Xiong, Dave, Amisha, Duic, Cameron, Kibe, Mercy, Kaminaka, Annette, Prasad, Minali, Siddig, Fares, Singer, Maxwell, Wong, Wendy, Jin, Qiao, Keenan, Tiarnan D. L., Hu, Xia, Chew, Emily Y., Lu, Zhiyong, Xu, Hua, Adelman, Ron A., Tham, Yih-Chung, Chen, Qingyu
Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies imple
Externí odkaz:
http://arxiv.org/abs/2409.13902
End-to-end relation extraction (E2ERE) is an important and realistic application of natural language processing (NLP) in biomedicine. In this paper, we aim to compare three prevailing paradigms for E2ERE using a complex dataset focused on rare diseas
Externí odkaz:
http://arxiv.org/abs/2311.13729
Autor:
Ai, Xuguang, Kavuluru, Ramakanth
End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or named entity recognition (NER))
Externí odkaz:
http://arxiv.org/abs/2304.01344
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Ai X; Department of Computer Science, University of Kentucky, Lexington, USA., Kavuluru R; Division of Biomedical Informatics, Dept. of Internal Medicine, University of Kentucky, Lexington, USA.
Publikováno v:
IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics [IEEE Int Conf Healthc Inform] 2023 Jun; Vol. 2023, pp. 610-618. Date of Electronic Publication: 2023 Dec 11.