Learning the Structure of Generative Models without Labeled Data.
Proc Mach Learn Res
; 70: 273-82, 2017 Aug.
Article
em En
| MEDLINE
| ID: mdl-30882087
ABSTRACT
Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model's dependency structure directly affects the quality of the estimated labels, but selecting a structure automatically without any labeled data is a distinct challenge. We propose a structure estimation method that maximizes the â 1-regularized marginal pseudolikelihood of the observed data. Our analysis shows that the amount of unlabeled data required to identify the true structure scales sublinearly in the number of possible dependencies for a broad class of models. Simulations show that our method is 100× faster than a maximum likelihood approach and selects 1/4 as many extraneous dependencies. We also show that our method provides an average of 1.5 F1 points of improvement over existing, user-developed information extraction applications on real-world data such as PubMed journal abstracts.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Proc Mach Learn Res
Ano de publicação:
2017
Tipo de documento:
Article