The generative process assumes that each entity mention arises from copying and optionally mutating an earlier name from a similar context.

Our model is an evolutionary generative process based on the name variation model of Andrews et al.

This can also relate seemingly dissimilar names via multiple steps in the generative process:

The IBPOT model defines a generative process for mappings between input and output forms based on three latent variables: the constraint violation matrices F (faithfulness) and M (markedness), and the weight vector w. The cells of the violation matrices correspond to the number of violations of a constraint by a given input-output mapping.

Represented constraint sampling We begin by resampling M j; for all represented constraints M.l, conditioned on the rest of the violations (M_(jl), F) and the weights w. This is the sampling counterpart of drawing existing features in the IBP generative process .

This is the sampling counterpart to the Poisson draw for new features in the IBP generative process .

The times reported are from the start of the generation process , eliminating variations due to interpreter startup, input parsing, etc.

Note that, as STRUCT is an anytime algorithm, valid sentences are available very early in the generation process , despite the size of the set of adjoining trees.

If so, we store it, and continue the generation process .

The generative process of word distributions for non-emotion topics follows the standard LDA definition with a scalar hyperparameter 607’).

We summarize the generative process of the EaLDA model as below:

As an alternative representation, the graphical model of the the generative process is shown by Figure 1.