783 research outputs found
Knowledge Base Population using Semantic Label Propagation
A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge
Bases for Natural Language Processin
Reliable Label Bootstrapping for Semi-Supervised Learning
Reducing the amount of labels required to train convolutional neural networks
without performance degradation is key to effectively reduce human annotation
efforts. We propose Reliable Label Bootstrapping (ReLaB), an unsupervised
preprossessing algorithm which improves the performance of semi-supervised
algorithms in extremely low supervision settings. Given a dataset with few
labeled samples, we first learn meaningful self-supervised, latent features for
the data. Second, a label propagation algorithm propagates the known labels on
the unsupervised features, effectively labeling the full dataset in an
automatic fashion. Third, we select a subset of correctly labeled (reliable)
samples using a label noise detection algorithm. Finally, we train a
semi-supervised algorithm on the extended subset. We show that the selection of
the network architecture and the self-supervised algorithm are important
factors to achieve successful label propagation and demonstrate that ReLaB
substantially improves semi-supervised learning in scenarios of very limited
supervision on CIFAR-10, CIFAR-100 and mini-ImageNet. We reach average error
rates of with 1 random labeled sample per class on
CIFAR-10 and lower this error to when the labeled sample in
each class is highly representative. Our work is fully reproducible:
https://github.com/PaulAlbert31/ReLaB.Comment: 10 pages, 3 figure
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