* traditional semantic parsers get around need for annotated logical forms by increasing number of logical predicates
* semantic parser must combine predicates into coherent logical form
* parser define few simple composition rules which over-generate and use model features to simulate soft rules and categories
* use POS tag features and features on denotations of predicted logical forms
* database is queried using logical language: lambda-dependency-based compositional semantics
* given utterance, semantic parser constructs a distribution over possible derivations, each derivation specifying application of a set of rules that culminates in the logical form at root of tree
* derivations constructed recursively by mapping natural language phrases to knowledge base predicates and small set of composition rules
* to produce manageable set of predicates per utterance, construct a lexicon that maps natural language phrases to logical predicates by aligning large text corpus to Freebase; also generate logical predicates compatible with neighboring predicates using bridging operation

  * 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