Index of papers in Proc. ACL 2009 that mention
  • CCG
Boxwell, Stephen and Mehay, Dennis and Brew, Chris
Abstract
CCG affords ways to augment treepath-based features to overcome these data sparsity issues.
Abstract
By adding features over CCG word-word dependencies and lexicalized verbal subcategorization frames (“supertags”), we can obtain an F-score that is substantially better than a previous CCG-based SRL system and competitive with the current state of the art.
Combinatory Categorial Grammar
Rather than using standard part-of-speech tags and grammatical rules, CCG encodes much of the combinatory potential of each word by assigning a syntactically informative category.
Combinatory Categorial Grammar
Further, CCG has the advantage of a transparent interface between the way the words combine and their dependencies with other words.
Introduction
Brutus uses the CCG parser of (Clark and Curran, 2007, henceforth the C&C parser), Charniak’s parser (Chamiak, 2001) for additional CFG-based features, and MALT parser (Nivre et al., 2007) for dependency features, while (Punyakanok et al., 2008) use results from an ensemble of parses from Charniak’s Parser and a Collins parser (Collins, 2003; Bike], 2004).
Introduction
We do not employ a similar strategy due to the differing notions of constituency represented in our parsers ( CCG having a much more fluid notion of constituency and the MALT parser using a different approach entirely).
Introduction
In the following, we briefly introduce the CCG grammatical formalism and motivate its use in SRL (Sections 2—3).
Potential Advantages to using CCG
There are many potential advantages to using the CCG formalism in SRL.
CCG is mentioned in 39 sentences in this paper.
Topics mentioned in this paper:
Clark, Stephen and Curran, James R.
Abstract
We compare the CCG parser of Clark and Curran (2007) with a state-of-the-art Penn Treebank (PTB) parser.
Abstract
An accuracy comparison is performed by converting the CCG derivations into PTB trees.
Abstract
We show that the conversion is extremely difficult to perform, but are able to fairly compare the parsers on a representative subset of the PTB test section, obtaining results for the CCG parser that are statistically no different to those for the Berkeley parser.
Introduction
The second approach is to apply statistical methods to parsers based on linguistic formalisms, such as HPSG, LFG, TAG, and CCG , with the grammar being defined manually or extracted from a formalism-specific treebank.
Introduction
The formalism-based parser we use is the CCG parser of Clark and Curran (2007), which is based on CCGbank (Hockenmaier and Steedman, 2007), a CCG version of the Penn Treebank.
Introduction
The comparison focuses on accuracy and is performed by converting CCG derivations into PTB phrase-structure trees.
The CCG to PTB Conversion
shows that converting gold-standard CCG derivations into the GRs in DepBank resulted in an F-score of only 85%; hence the upper bound on the performance of the CCG parser, using this evaluation scheme, was only 85%.
The CCG to PTB Conversion
First, the corresponding derivations in the treebanks are not isomorphic: a CCG derivation is not simply a relabelling of the nodes in the PTB tree; there are many constructions, such as coordination and control structures, where the trees are a different shape, as well as having different labels.
CCG is mentioned in 30 sentences in this paper.
Topics mentioned in this paper: