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Found 6 papers, 0 papers with code
Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text
We evaluate the performance of our models on the biomedical entity linking benchmarks using MedMentions and BC5CDR datasets.
On Masked Language Models for Contextual Link Prediction
In the real world, many relational facts require context; for instance, a politician holds a given elected position only for a particular timespan.
Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval
A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms, an initial candidate retrieval stage that generates a shortlist of entities for each mention, followed by a candidate ranking stage.
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations
In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manifold transformations translates from the Riemannian to the pseudo-Riemannian case.
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space.
Stabilizing Generative Adversarial Networks: A Survey
Generative Adversarial Networks (GANs) are a type of generative model which have received much attention due to their ability to model complex real-world data.
