This model leverages the CCG combinatory operators to guide a nonlinear transformation of meaning within a sentence.

In this paper we focus on CCG , a linguistically expressive yet computationally efficient grammar formalism.

CCG relies on combinatory logic (as opposed to lambda calculus) to build its expressions.

CCG has been described as having a transparent surface between the syntactic and the seman-

in this field includes the Combinatory Categorial Grammar ( CCG ), which also places increased emphasis on syntactic coverage (Szabolcsi, 1989).

We achieve this goal by employing the CCG formalism to consider compositional structures at any point in a parse tree.

CCG is attractive both for its transparent interface between syntax and semantics, and a small but powerful set of combinatory operators with which we can parametrise our nonlinear transformations of compositional meaning.

This paper describes a method of inducing wide-coverage CCG resources for Japanese.

Our method first integrates multiple dependency-based corpora into phrase structure trees and then converts the trees into CCG derivations.

combinatory categorial grammar ( CCG ) (Steedman, 2001).

Our work is basically an extension of a seminal work on CCGbank (Hockenmaier and Steedman, 2007), in which the phrase structure trees of the Penn Treebank (PTB) (Marcus et al., 1993) are converted into CCG derivations and a wide-coverage CCG lexicon is then extracted from these derivations.

Moreover, the relation between chunk-based dependency structures and CCG derivations is not obvious.

We are interested in parsing constituency-based grammars such as HPSG and CCG using a small amount of data specific for the target formalism, and a large quantity of coarse CFG annotations from the Penn Treebank.

We evaluate our approach on three constituency-based grammars — CCG , HPSG, and LPG, augmented with the Penn Treebank—l.

A natural candidate for such coarse annotations is context-free grammar (CFG) from the Penn Treebank, while the target formalism can be any constituency-based grammars, such as Combinatory Categorial Grammar ( CCG ) (Steedman, 2001), Lexical Functional Grammar (LFG) (Bresnan, 1982) or Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994).

We evaluate our approach on three constituency-based grammars — CCG , HPSG, and LPG.

S CFG Sldcll CCG

There have been several attempts to map annotations in coarse grammars like CFG to annotations in richer grammar, like HPSG, LFG, or CCG .

For instance, Hockenmaier and Steedman (2002) made thousands of POS and constituent modifications to the Penn Treebank to facilitate transfer to CCG .

Recall that our goal is to learn how to parse the target formalisms while using two annotated sources: a small set of sentences annotated in the target formalism (e.g., CCG ), and a large set of sentences with coarse annotations.

For simplicity we focus on the CCG formalism in what follows.

Using a fixed CCG grammar and a procedure based on unification in second-order logic, UBL learns a lexicon A from the training data which includes entries like:

Example CCG Grammar Rules

technique does require manual specification of rules that construct CCG lexical entries from dependency parses.

In comparison, we fully automate the process of constructing CCG lexical entries for the semantic parser by making it a prediction task.