Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Kyle, Lo"'
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-11 (2022)
Measurement(s) Text Technology Type(s) Natural Language
Externí odkaz:
https://doaj.org/article/25823f24da1c4b53b29fbdf801fa6761
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 10, Pp 376-392 (2022)
AbstractAccurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position o
Externí odkaz:
https://doaj.org/article/8391c75e305f49999ff12c1d2cd19316
Publikováno v:
ACM Transactions on Mathematical Software. 49:1-12
For control nets outlining a large class of topological polyhedra, not just tensor-product grids, bi-cubic polyhedral splines form a piecewise polynomial, first-order differentiable space that associates one function with each vertex. Akin to tensor-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40db24924285bac6d8390774c0694037
https://doi.org/10.1145/3570158
https://doi.org/10.1145/3570158
Autor:
Anshul Kanakia, Kuansan Wang, Yuxiao Dong, Boya Xie, Kyle Lo, Zhihong Shen, Lucy Lu Wang, Chiyuan Huang, Darrin Eide, Sebastian Kohlmeier, Chieh-Han Wu
Publikováno v:
Frontiers in Research Metrics and Analytics, Vol 5 (2020)
On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering sy
Externí odkaz:
https://doaj.org/article/d101f9223242447b988af9a2fcd79a64
Publikováno v:
ACM Transactions on Computer-Human Interaction.
When seeking information not covered in patient-friendly documents, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To improve access to medical papers, we explore four featu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28070ee877e0062eecf7b44b5f534b3d
https://doi.org/10.1145/3589955
https://doi.org/10.1145/3589955
Autor:
Raymond Fok, Hita Kambhamettu, Luca Soldaini, Jonathan Bragg, Kyle Lo, Marti Hearst, Andrew Head, Daniel S Weld
Publikováno v:
Proceedings of the 28th International Conference on Intelligent User Interfaces.
Researchers need to keep up with immense literatures, though it is time-consuming and difficult to do so. In this paper, we investigate the role that intelligent interfaces can play in helping researchers skim papers, that is, rapidly reviewing a pap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c071e177aab55e012b5e75c2d6665aa1
https://doi.org/10.1145/3581641.3584034
https://doi.org/10.1145/3581641.3584034
Autor:
Michael Cafarella, Michael Anderson, Iz Beltagy, Arie Cattan, Sarah Chasins, Ido Dagan, Doug Downey, Oren Etzioni, Sergey Feldman, Tian Gao, Tom Hope, Kexin Huang, Sophie Johnson, Daniel King, Kyle Lo, Yuze Lou, Matthew Shapiro, Dinghao Shen, Shivashankar Subramanian, Lucy Lu Wang, Yuning Wang, Yitong Wang, Daniel S. Weld, Jenny Vo‐Phamhi, Anna Zeng, Jiayun Zou
Publikováno v:
AI Magazine; Vol. 43 No. 1: Spring 2022; 59-68
The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query-answering applications. The leading example of a public, general-purpose open knowledge network (aka knowledge gra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3eb7fb8700627d9bbcf14b111818bcc
https://doi.org/10.1609/aimag.v43i1.19126
https://doi.org/10.1609/aimag.v43i1.19126
Autor:
Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg, Andrew Head, Kyle Lo, Doug Downey, Daniel S. Weld
When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during litera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3d658222f1634926c446a3d4c6ac619
http://arxiv.org/abs/2302.07302
http://arxiv.org/abs/2302.07302
Autor:
Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Šaško, Quentin Lhoest, Angelina Mcmillan-Major, Gérard Dupont, Stella Biderman, Anna Rogers, Loubna Ben Allal, Francesco de Toni, Giada Pistilli, Olivier Nguyen, Somaieh Nikpoor, Maraim Masoud, Pierre Colombo, Javier de la Rosa, Paulo Villegas, Tristan Thrush, Shayne Longpre, Sebastian Nagel, Leon Weber, Manuel Romero Muñoz, Jian Zhu, Daniel van Strien, Zaid Alyafeai, Khalid Almubarak, Vu Minh Chien, Itziar Gonzalez-Dios, Aitor Soroa, Kyle Lo, Manan Dey, Pedro Ortiz Suarez, Aaron Gokaslan, Shamik Bose, David Ifeoluwa Adelani, Long Phan, Hieu Tran, Ian Yu, Suhas Pai, Jenny Chim, Violette Lepercq, Suzana Ilić, Margaret Mitchell, Sasha Luccioni, Yacine Jernite
Publikováno v:
HAL
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de4f6aa98013be0d5a61119e2bb20f3a
Autor:
Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, Lucy Wang
Publikováno v:
Wright, D, Wadden, D, Lo, K, Kuehl, B, Cohan, A, Augenstein, I & Wang, L L 2022, Generating Scientific Claims for Zero-Shot Scientific Fact Checking . in Generating Scientific Claims for Zero-Shot Scientific Fact Checking . Association for Computational Linguistics, 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 23/05/2022 . https://doi.org/10.18653/v1/2022.acl-long.175
Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of significant amounts of training data, as annotation requires domain expertise. To address this challenge, we propose scientific claim generatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::207da191730e76de06a43f07444f95b8
https://doi.org/10.18653/v1/2022.acl-long.175
https://doi.org/10.18653/v1/2022.acl-long.175