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 .

While all of the target formalisms share a similar basic syntactic structure with Penn Treebank CFG, they also encode additional constraints and semantic features.

The standard solution to this bottleneck has relied on manually crafted transformation rules that map readily available syntactic annotations (e.g, the Penn Treebank ) to the desired formalism.

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).

All of these formalisms share a similar basic syntactic structure with Penn Treebank CFG.

For instance, mappings may specify how to convert traces and functional tags in Penn Treebank to the f-structure in LFG (Cahill, 2004).

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

21,938 total examples, 15,330 come from sections 2—21 of the Penn Treebank (Marcus et al., 1993).

For the Penn Treebank , we extracted the examples using the provided gold standard parse trees, whereas, for the latter cases, we used the output of an open source parser (Tratz and Hovy, 2011).

The accuracy figures for the test instances from the Penn Treebank , The Jungle Book, and The History of the Decline and Fall of the Roman Empire were 88.8%, 84.7%, and 80.6%, respectively.

The NomBank project (Meyers et al., 2004) provides coarse annotations for some of the possessive constructions in the Penn Treebank , but only those that meet their criteria.

Penn Treebank , respectively.

portion of the Penn Treebank .

The Penn Treebank and The History of the Decline and Fall of the R0-man Empire were substantially more similar, although there are notable differences.

We perform parsing experiments the Penn Treebank and draw comparisons to Tree-Substitution Grammars and between different variations in probabilistic model design.

As a proof of concept, we investigate OSTAG in the context of the classic Penn Treebank statistical parsing setup; training on section 2-21 and testing on section 23.

Furthermore, the various parameteri-zations of adjunction with OSTAG indicate that, at least in the case of the Penn Treebank , the finer grained modeling of a full table of adjunction probabilities for each Goodman index OSTAG3 overcomes the danger of sparse data estimates.

We evaluate OSTAG on the familiar task of parsing the Penn Treebank .

We propose a simple but empirically effective heuristic for grammar induction for our experiments on Penn Treebank data.

We trained our model on sections 2—21 of the WSJ part of the Penn Treebank (Marcus et al., 1999).

Unfortunately, marking for argument/modifiers in the Penn Treebank is incomplete, and is limited to certain adverbials, e.g.

This corpus adds annotations indicating, for each node in the Penn Treebank , whether that node is a modifier.

The experiments are conducted on Penn Treebank Wall Street Journal corpus.

Because we are trying to improve (Yatbaz et al., 2012), we select the experiment on Penn Treebank Wall Street Journal corpus in that work as our baseline and replicate it.

For instance,the gold tag perplexity of word “offers” in the Penn Treebank Wall Street Journal corpus we worked on equals to 1.966.

For example, Higgins and Sadock (2003) find fewer than 1000 sentences with two or more explicit quantifiers in the Wall Street journal section of Penn Treebank .

Plurals form 18% of the NPs in our corpus and 20% of the nouns in Penn Treebank .

Explicit universals, on the other hand, form less than 1% of the determiners in Penn Treebank .

Labeled English data employed in this paper were derived from the Wall Street Journal (WSJ) corpus of the Penn Treebank (Marcus et al., 1993).

In addition, we removed from the unlabeled English data the sentences that appear in the WSJ corpus of the Penn Treebank .

On standard evaluations using both the Penn Treebank and the Penn Chinese Treebank, our parser gave higher accuracies than the Berkeley parser (Petrov and Klein, 2007), a state-of-the-art chart parser.