26 research outputs found
Deep Joint Entity Disambiguation with Local Neural Attention
We propose a novel deep learning model for joint document-level entity
disambiguation, which leverages learned neural representations. Key components
are entity embeddings, a neural attention mechanism over local context windows,
and a differentiable joint inference stage for disambiguation. Our approach
thereby combines benefits of deep learning with more traditional approaches
such as graphical models and probabilistic mention-entity maps. Extensive
experiments show that we are able to obtain competitive or state-of-the-art
accuracy at moderate computational costs.Comment: Conference on Empirical Methods in Natural Language Processing
(EMNLP) 2017 long pape
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Representing graphs as sets of node embeddings in certain curved Riemannian
manifolds has recently gained momentum in machine learning due to their
desirable geometric inductive biases, e.g., hierarchical structures benefit
from hyperbolic geometry. However, going beyond embedding spaces of constant
sectional curvature, while potentially more representationally powerful, proves
to be challenging as one can easily lose the appeal of computationally
tractable tools such as geodesic distances or Riemannian gradients. Here, we
explore computationally efficient matrix manifolds, showcasing how to learn and
optimize graph embeddings in these Riemannian spaces. Empirically, we
demonstrate consistent improvements over Euclidean geometry while often
outperforming hyperbolic and elliptical embeddings based on various metrics
that capture different graph properties. Our results serve as new evidence for
the benefits of non-Euclidean embeddings in machine learning pipelines.Comment: Submitted to the Thirty-fourth Conference on Neural Information
Processing System