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CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank

What it is about

The paper gives an algorithm that translates Penn Treebank phrase-structure trees into Combinatory Categorial Grammar derivations, with binarization, head and complement/adjunct decisions, and word-word dependencies, including long-range ones from extraction, control, raising, right-node raising and coordination. Before translation the Penn annotation is corrected for noise and inconsistencies. The resulting corpus, CCGbank, covers 48,934 of the 49,208 Penn sentences (99.44%). The rule instantiations are Zipfian: in sections 02–21, 3,262 instantiated rules occur, and the ten most frequent cover 59.2% of all instances. Rule coverage on the unseen section 00 is 51,932 of 51,984 instances.

Ideas

For Mova

The long-range relations of I1 belong to 25-relative-clauses, 23-complementation and 24-coordination-subordination (right-node raising). The code is the prag and world crates, en/src/convert.rs and tools/treeagree. Measure first: the rejection counts of I2, since they show what the converters lose before any model is trained.

Topics: Dependency parsing · Treebanks

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/coli.2007.33.3.355

@article{hockenmaier2007ccgbank,
  title = {{CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank}},
  author = {Hockenmaier, Julia and Steedman, Mark},
  journal = {Computational Linguistics},
  year = {2007},
  doi = {10.1162/coli.2007.33.3.355},
  url = {https://doi.org/10.1162/coli.2007.33.3.355}
}

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