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class CcgBankDatasetReader(DatasetReader):
 | def __init__(
 |     self,
 |     token_indexers: Dict[str, TokenIndexer] = None,
 |     tag_label: str = "ccg",
 |     feature_labels: Sequence[str] = (),
 |     label_namespace: str = "labels",
 |     **kwargs
 | ) -> None

Reads data in the "machine-readable derivation" format of the CCGbank dataset. (see, section D.2)

In particular, it pulls out the leaf nodes, which are represented as

(<L ccg_category modified_pos original_pos token predicate_arg_category>)

The tarballed version of the dataset contains many files worth of this data, in files /data/AUTO/xx/

This dataset reader expects a single text file. Accordingly, if you're using that dataset, you'll need to first concatenate some of those files into a training set, a validation set, and a test set.

Registered as a DatasetReader with name "ccgbank".


  • token_indexers : Dict[str, TokenIndexer], optional (default = {"tokens": SingleIdTokenIndexer()})
    We use this to define the input representation for the text. See TokenIndexer. Note that the output tags will always correspond to single token IDs based on how they are pre-tokenised in the data file.
  • tag_label : str, optional (default = ccg)
    Specify ccg, modified_pos, original_pos, or predicate_arg to have that tag loaded into the instance field tag.
  • feature_labels : Sequence[str], optional (default = ())
    These labels will be loaded as features into the corresponding instance fields: ccg -> ccg_tags, modified_pos -> modified_pos_tags, original_pos -> original_pos_tags, or predicate_arg -> predicate_arg_tags Each will have its own namespace : ccg_tags, modified_pos_tags, original_pos_tags, predicate_arg_tags. If you want to use one of the tags as a feature in your model, it should be specified here.
  • label_namespace : str, optional (default = labels)
    Specifies the namespace for the chosen tag_label.


class CcgBankDatasetReader(DatasetReader):
 | ...
 | def text_to_instance(
 |     self,
 |     tokens: List[str],
 |     ccg_categories: List[str] = None,
 |     original_pos_tags: List[str] = None,
 |     modified_pos_tags: List[str] = None,
 |     predicate_arg_categories: List[str] = None
 | ) -> Instance

We take pre-tokenized input here, because we don't have a tokenizer in this class.


  • tokens : List[str]
    The tokens in a given sentence.
  • ccg_categories : List[str], optional (default = None)
    The CCG categories for the words in the sentence. (e.g. N/N)
  • original_pos_tags : List[str], optional (default = None)
    The tag assigned to the word in the Penn Treebank.
  • modified_pos_tags : List[str], optional (default = None)
    The POS tag might have changed during the translation to CCG.
  • predicate_arg_categories : List[str], optional (default = None)
    Encodes the word-word dependencies in the underlying predicate- argument structure.


  • An Instance containing the following fields:
    tokens : TextField The tokens in the sentence. tags : SequenceLabelField The tags corresponding to the tag_label constructor argument. feature_label_tags : SequenceLabelField Tags corresponding to each feature_label (if any) specified in the feature_labels constructor argument.