Search Results for author:
Found 11 papers, 6 papers with code
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.
Entity Linking via Explicit Mention-Mention Coreference Modeling
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
Polynomial Precision Dependence Solutions to Alignment Research Center Matrix Completion Problems
We present solutions to the matrix completion problems proposed by the Alignment Research Center that have a polynomial dependence on the precision $\varepsilon$.
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
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.
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
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.
Entity Linking and Discovery via Arborescence-based Supervised Clustering
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.
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
Sampling is an established technique to scale graph neural networks to large graphs.
Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology
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
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.
Clustering-based Inference for Biomedical Entity Linking
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.
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
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.
