Search Results for author: Rico Angell

Found 9 papers, 5 papers with code

Entity Linking via Explicit Mention-Mention Coreference Modeling

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 MedMentions (Recall@64 metric)

Entity Linking Re-Ranking

Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

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.

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization

1 code implementation23 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.

Retrieval

Entity Linking and Discovery via Arborescence-based Supervised Clustering

1 code implementation2 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.

Entity Linking

Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology

1 code implementation26 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.

Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models

1 code implementation17 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.

BIG-bench Machine Learning Fairness

Clustering-based Inference for Biomedical Entity Linking

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.

Entity Linking

Inferring Latent Velocities from Weather Radar Data using Gaussian Processes

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.

Gaussian Processes

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