The goal of semantic parsing is to map text to a complete and detailed meaning representation (Mooney, 2007).

The standard language for meaning representation is first-order logic or a sublanguage, such as FunQL (Kate et al., 2005; Clarke et al., 2010) and lambda calculus (Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007).

Poon & Domingos (2009, 2010) induce a meaning representation by clustering synonymous lambda-calculus forms stemming from partitions of dependency trees.

This paper introduces grounded unsupervised semantic parsing, which leverages available database for indirect supervision and uses a grounded meaning representation to account for syntax-semantics mismatch in dependency-based semantic parsing.

To combat this problem, GUSP introduces a novel dependency-based meaning representation with an augmented state space to account for semantic relations that are nonlocal in the dependency tree.

However, GUSP uses a different meaning representation defined over individual nodes and edges, rather than partitions, which enables linear-time exact inference.

Their approach alleviates some complexity in the meaning representation for handling syntax-semantics mismatch, but it has to search over a much larger search space involving exponentially many candidate trees.

Semantic parsing maps text to a formal meaning representation such as logical forms or structured queries.

To handle syntax-semantics mismatch, GUSP introduces a novel dependency-based meaning representation

Semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance.

We have presented a semantic parser which uses techniques from machine translation to learn mappings from natural language to variable-free meaning representations .

Semantic parsing (SP) is the problem of transforming a natural language (NL) utterance into a machine-interpretable meaning representation (MR).

At least superficially, SP is simply a machine translation (MT) task: we transform an NL utterance in one language into a statement of another (unnatural) meaning representation language (MRL).

Linearization We assume that the MRL is variable-free (that is, the meaning representation for each utterance is tree-shaped), noting that for-malisms with variables, like the A-calculus, can be mapped onto variable-free logical forms with combinatory logics (Curry et al., 1980).

Other work which generalizes from variable-free meaning representations to A-calculus expressions includes the natural language generation procedure described by Lu and Ng (2011).

The experiment shows that it can correctly learn the meaning representations in terms of HMM parameters for our lexical entries, from highly ambiguous training data.

Language is grounded by mapping words, phrases, and sentences to meaning representations referring to the world.

Dominey and Boucher (2005) paired narrated sentences with symbolic representations of their meanings, automatically extracted from video, to learn object names, spatial-relation terms, and event names as a mapping from the grammatical structure of a sentence to the semantic structure of the associated meaning representation .

Chen and Mooney (2008) learned the language of sportscasting by determining the mapping between game commentaries and the meaning representations output by a rule-based simulation of the game.