ACL
2012
12 years 2 months ago
2012
In relation extraction, distant supervision seeks to extract relations between entities from text by using a knowledge base, such as Freebase, as a source of supervision. When a s...
JMLR
2012
12 years 2 months ago
2012
We propose a new family of latent variable models called max-margin min-entropy (m3e) models, which define a distribution over the output and the hidden variables conditioned on ...
JMLR
2012
12 years 2 months ago
2012
The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with hidden variables. However, the algorithm has several non-trivial limitat...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
We have recently proposed an EM-style algorithm to optimize log-linear models with hidden variables. In this paper, we use this algorithm to optimize a hidden conditional random ...
JMLR
2010
13 years 7 months ago
2010
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
ACL
2009
13 years 10 months ago
2009
Variational EM has become a popular technique in probabilistic NLP with hidden variables. Commonly, for computational tractability, we make strong independence assumptions, such a...
NAACL
2010
13 years 10 months ago
2010
In this paper, we present a dependency treebased method for sentiment classification of Japanese and English subjective sentences using conditional random fields with hidden varia...
UAI
1996
14 years 1 months ago
1996
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
UAI
2003
14 years 1 months ago
2003
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is usin...
UAI
2004
14 years 1 months ago
2004
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive proced...