class typing.Dict[str,] = None, use_pos_tags: bool = True, lazy: bool = False) → None[source]


Reads constituency parses from the WSJ part of the Penn Tree Bank from the LDC. This DatasetReader is designed for use with a span labelling model, so it enumerates all possible spans in the sentence and returns them, along with gold labels for the relevant spans present in a gold tree, if provided.

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.

use_pos_tags : bool, optional, (default = True)

Whether or not the instance should contain gold POS tags as a field.

lazy : bool, optional, (default = False)

Whether or not instances can be consumed lazily.

classmethod from_params(params: allennlp.common.params.Params) →[source]
text_to_instance(tokens: typing.List[str], pos_tags: typing.List[str] = None, gold_tree: nltk.tree.Tree = None) →[source]

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

tokens : List[str], required.

The tokens in a given sentence.

pos_tags ``List[str]``, optional, (default = None).

The POS tags for the words in the sentence.

gold_tree : Tree, optional (default = None).

The gold parse tree to create span labels from.

An ``Instance`` containing the following fields:
tokens : TextField

The tokens in the sentence.

pos_tags : SequenceLabelField

The POS tags of the words in the sentence. Only returned if use_pos_tags is True

spans : ListField[SpanField]

A ListField containing all possible subspans of the sentence.

span_labels : SequenceLabelField, optional.

The constiutency tags for each of the possible spans, with respect to a gold parse tree. If a span is not contained within the tree, a span will have a NO-LABEL label.

gold_tree : MetadataField(Tree)

The gold NLTK parse tree for use in evaluation.