1 code implementation • NAACL 2022 • Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions.
Ranked #1 on Entity Linking on ZESHEL
no code implementations • CRAC (ACL) 2021 • Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum
Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.
no code implementations • 8 Jan 2024 • Rico Angell
We present solutions to the matrix completion problems proposed by the Alignment Research Center that have a polynomial dependence on the precision $\varepsilon$.
1 code implementation • 19 Dec 2023 • Rico Angell, Andrew McCallum
We present Unified Spectral Bundling with Sketching (USBS), a provably correct, fast and scalable algorithm for solving massive SDPs that can leverage a warm-start initialization to further accelerate convergence.
1 code implementation • 23 Oct 2022 • Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum
When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods.
1 code implementation • 2 Sep 2021 • Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions.
no code implementations • 28 May 2021 • Arthur Feeney, Rishabh Gupta, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari, Tengfei Ma
Sampling is an established technique to scale graph neural networks to large graphs.
1 code implementation • 26 Jan 2021 • Sunil Mohan, Rico Angell, Nick Monath, Andrew McCallum
Tools to explore scientific literature are essential for scientists, especially in biomedicine, where about a million new papers are published every year.
1 code implementation • 17 Dec 2020 • Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen J. Giguere, Yuriy Brun
To address bias in machine learning, data scientists need tools that help them understand the trade-offs between model quality and fairness in their specific data domains.
no code implementations • NAACL 2021 • Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum
In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.
no code implementations • NeurIPS 2018 • Rico Angell, Daniel R. Sheldon
Archived data from the US network of weather radars hold detailed information about bird migration over the last 25 years, including very high-resolution partial measurements of velocity.