Approximating posterior distributions in belief networks using mixtures
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Convex Optimization
Inside-outside reestimation from partially bracketed corpora
HLT '91 Proceedings of the workshop on Speech and Natural Language
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Annealing structural bias in multilingual weighted grammar induction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
What's with the attitude?: identifying sentences with attitude in online discussions
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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Variational EM has become a popular technique in probabilistic NLP with hidden variables. Commonly, for computational tractability, we make strong independence assumptions, such as the mean-field assumption, in approximating posterior distributions over hidden variables. We show how a looser restriction on the approximate posterior, requiring it to be a mixture, can help inject prior knowledge to exploit soft constraints during the variational E-step.