: NLP Vocabulary: expectation–maximization algorithm

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expectation–maximization algorithm

An iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. (Wikipedia).

Otherwise parameters must be learned using approximate inference algorithms (e.g. Gibbs sampling variational inference) since exact Expectation-Maximization (EM) algorithm is computationally intractable (Ghahramani and Jordan 1997). (Duh, 2005)
The idea behind the DMV model is to estimate the syntactic tree by using the Expectation-Maximization (EM) algorithm. (da Silva & Pardo, 2024)
The resulting family of algorithms includes the expectation-maximization algorithm (EM) and its variant Viterbi EM as well as a so-called softmax-EM algorithm. (Tu & Honavar, 2012)
This paper discusses the supervised learning of morphology using stochastic transducers trained using the Expectation-Maximization (EM) algorithm. (Clark, 2002)
To do so we learn the feature vectors and adjust their weight vectors by using the Expectation-Maximization (EM) algorithm on the training data. (Zhang, Wang & Lepage, 2016)

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