Zobrazeno 1 - 10
of 291
pro vyhledávání: '"George Arthur"'
Autor:
Ebenezer Krampah Aidoo, Frank Twum Aboagye, George Edem Agginie, Felix Abekah Botchway, George Osei-Adjei, Michael Appiah, Ruth Duku Takyi, Samuel Asamoah Sakyi, Linda Amoah, George Arthur, Bernard Walter Lawson, Richard Harry Asmah, Paul Boateng, Otubea Ansah, Karen Angeliki Krogfelt
Publikováno v:
Malaria Journal, Vol 23, Iss 1, Pp 1-11 (2024)
Abstract Background Progress toward malaria elimination is increasing as many countries near zero indigenous malaria cases. In settings nearing elimination, interventions will be most effective at interrupting transmission when targeted at the residu
Externí odkaz:
https://doaj.org/article/130a7dc61bb3499b8c4e94f03141d6e8
Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing
Autor:
Ahmed, Shafiuddin Rehan, Wang, Zhiyong Eric, Baker, George Arthur, Stowe, Kevin, Martin, James H.
The most popular Cross-Document Event Coreference Resolution (CDEC) datasets fail to convey the true difficulty of the task, due to the lack of lexical diversity between coreferring event triggers (words or phrases that refer to an event). Furthermor
Externí odkaz:
http://arxiv.org/abs/2407.11988
Autor:
Yuqi Wang, Sarah R. Allred, Emily A. Greenfield, Aayush Yadav, Ryan Pletcher, George Arthur, Sachin Saxena, Trista Harig, Emily Rankin, Benjamin Rudolph, Ummulkhayer Sameha, Shwetal Sharma, Shibin Yan
Publikováno v:
Data in Brief, Vol 38, Iss , Pp 107426- (2021)
Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, i
Externí odkaz:
https://doaj.org/article/3b391949fb8a4324aa0b93c66215d4b4
Autor:
Ahmed, Shafiuddin Rehan, Baker, George Arthur, Judge, Evi, Regan, Michael, Wright-Bettner, Kristin, Palmer, Martha, Martin, James H.
Event Coreference Resolution (ECR) as a pairwise mention classification task is expensive both for automated systems and manual annotations. The task's quadratic difficulty is exacerbated when using Large Language Models (LLMs), making prompt enginee
Externí odkaz:
http://arxiv.org/abs/2404.08656