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srl

allennlp_models.structured_prediction.dataset_readers.srl

[SOURCE]

SrlReader#

@DatasetReader.register("srl")
class SrlReader(DatasetReader):
 | def __init__(
 |     self,
 |     token_indexers: Dict[str, TokenIndexer] = None,
 |     domain_identifier: str = None,
 |     bert_model_name: str = None,
 |     **kwargs
 | ) -> None

This DatasetReader is designed to read in the English OntoNotes v5.0 data for semantic role labelling. It returns a dataset of instances with the following fields:

tokens : TextField The tokens in the sentence. verb_indicator : SequenceLabelField A sequence of binary indicators for whether the word is the verb for this frame. tags : SequenceLabelField A sequence of Propbank tags for the given verb in a BIO format.

Parameters

  • token_indexers : Dict[str, TokenIndexer], optional
    We similarly use this for both the premise and the hypothesis. See TokenIndexer. Default is {"tokens": SingleIdTokenIndexer()}.
  • domain_identifier : str, optional (default = None)
    A string denoting a sub-domain of the Ontonotes 5.0 dataset to use. If present, only conll files under paths containing this domain identifier will be processed.
  • bert_model_name : Optional[str], optional (default = None)
    The BERT model to be wrapped. If you specify a bert_model here, then we will assume you want to use BERT throughout; we will use the bert tokenizer, and will expand your tags and verb indicators accordingly. If not, the tokens will be indexed as normal with the token_indexers.

Returns

  • A Dataset of Instances for Semantic Role Labelling.

text_to_instance#

class SrlReader(DatasetReader):
 | ...
 | def text_to_instance(
 |     self,
 |     tokens: List[Token],
 |     verb_label: List[int],
 |     tags: List[str] = None
 | ) -> Instance

We take pre-tokenized input here, along with a verb label. The verb label should be a one-hot binary vector, the same length as the tokens, indicating the position of the verb to find arguments for.