Search Results for author:
Found 5 papers, 1 papers with code
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.
Retrieve to Explain: Evidence-driven Predictions with Language Models
R2E is a retrieval-based language model that prioritizes amongst a pre-defined set of possible answers to a research question based on the evidence in a document corpus, using Shapley values to identify the relative importance of pieces of evidence to the final prediction.
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.
