1,480 research outputs found
Neural Collective Entity Linking
Entity Linking aims to link entity mentions in texts to knowledge bases, and
neural models have achieved recent success in this task. However, most existing
methods rely on local contexts to resolve entities independently, which may
usually fail due to the data sparsity of local information. To address this
issue, we propose a novel neural model for collective entity linking, named as
NCEL. NCEL applies Graph Convolutional Network to integrate both local
contextual features and global coherence information for entity linking. To
improve the computation efficiency, we approximately perform graph convolution
on a subgraph of adjacent entity mentions instead of those in the entire text.
We further introduce an attention scheme to improve the robustness of NCEL to
data noise and train the model on Wikipedia hyperlinks to avoid overfitting and
domain bias. In experiments, we evaluate NCEL on five publicly available
datasets to verify the linking performance as well as generalization ability.
We also conduct an extensive analysis of time complexity, the impact of key
modules, and qualitative results, which demonstrate the effectiveness and
efficiency of our proposed method.Comment: 12 pages, 3 figures, COLING201
Optimized Decision Making in the Power Industry Using Machine Learning
To supply power to customers, a power company procures fuel from suppliers while also holding an inventory of such fuel and/or storing a very limited amount of excess power in a battery. Additionally, a power company may directly sell renewable energy sources to customers and exchange power with a neighboring grid to ameliorate shortages. The standard techniques of optimizing profit entail a tedious, human-driven decision-making procedure that results only in local optima. This disclosure optimizes the profit of a power company by automatically making intelligent procurement and selling decisions using machine learning. The decisions are treated as an end-to-end supply-chain problem and jointly optimized, such that optimal trade-offs are achieved amongst supply, demand, system restrictions, and environmental constraints. In particular, the techniques jointly optimize over the end-to-end supply chain, including fuel procurement, fuel and electricity selling, fuel stocking, etc
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Joint representation learning of words and entities benefits many NLP tasks,
but has not been well explored in cross-lingual settings. In this paper, we
propose a novel method for joint representation learning of cross-lingual words
and entities. It captures mutually complementary knowledge, and enables
cross-lingual inferences among knowledge bases and texts. Our method does not
require parallel corpora, and automatically generates comparable data via
distant supervision using multi-lingual knowledge bases. We utilize two types
of regularizers to align cross-lingual words and entities, and design knowledge
attention and cross-lingual attention to further reduce noises. We conducted a
series of experiments on three tasks: word translation, entity relatedness, and
cross-lingual entity linking. The results, both qualitatively and
quantitatively, demonstrate the significance of our method.Comment: 11 pages, EMNLP201
A Fast Smoothing Newton Method for Bilevel Hyperparameter Optimization for SVC with Logistic Loss
Support Vector Classification with logistic loss has excellent theoretical
properties in classification problems where the label values are not
continuous. In this paper, we reformulate the hyperparameter selection for SVC
with logistic loss as a bilevel optimization problem in which the upper-level
problem and the lower-level problem are both based on logistic loss. The
resulting bilevel optimization model is converted to a single-level nonlinear
programming (NLP) problem based on the KKT conditions of the lower-level
problem. Such NLP contains a set of nonlinear equality constraints and a simple
lower bound constraint. The second-order sufficient condition is characterized,
which guarantees that the strict local optimizers are obtained. To solve such
NLP, we apply the smoothing Newton method proposed in \cite{Liang} to solve the
KKT conditions, which contain one pair of complementarity constraints. We show
that the smoothing Newton method has a superlinear convergence rate. Extensive
numerical results verify the efficiency of the proposed approach and strict
local minimizers can be achieved both numerically and theoretically. In
particular, compared with other methods, our algorithm can achieve competitive
results while consuming less time than other methods.Comment: 27 page
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