* Efficiently find fraction of referring expressions for scenes that are used; estimate associated likelihoods
  * Learn probability distribution over set of logical expressions that select a target set of objects in a world state
  * Model as globally normalized log-linear model using features of logical form *z*
  * Distinction for plural entities in generated logical forms
  * globally optimized log-linear model, conditioned on state S and set of target objects G
  * Three kinds of features: logical expression structure features, situated features, and a complexity feature
  * learning two models--one for a global logical form for world-state model; and one learning a series of classifiers for each 
  * learn codebooks and associated sparse codes