class Dict[str,] = None, lazy: bool = False)[source]


Reads a file in the SemEval 2015 Task 18 (Broad-coverage Semantic Dependency Parsing) format.

token_indexersDict[str, TokenIndexer], optional (default=``{“tokens”: SingleIdTokenIndexer()}``)

The token indexers to be applied to the words TextField.

text_to_instance(self, tokens: List[str], pos_tags: List[str] = None, arc_indices: List[Tuple[int, int]] = None, arc_tags: List[str] = None) →[source]

Does whatever tokenization or processing is necessary to go from textual input to an Instance. The primary intended use for this is with a Predictor, which gets text input as a JSON object and needs to process it to be input to a model.

The intent here is to share code between _read() and what happens at model serving time, or any other time you want to make a prediction from new data. We need to process the data in the same way it was done at training time. Allowing the DatasetReader to process new text lets us accomplish this, as we can just call DatasetReader.text_to_instance when serving predictions.

The input type here is rather vaguely specified, unfortunately. The Predictor will have to make some assumptions about the kind of DatasetReader that it’s using, in order to pass it the right information. str)[source] str) → Tuple[List[Dict[str, str]], List[Tuple[int, int]], List[str]][source]

Parses a chunk of text in the SemEval SDP format.

Each word in the sentence is returned as a dictionary with the following format: ‘id’: ‘1’, ‘form’: ‘Pierre’, ‘lemma’: ‘Pierre’, ‘pos’: ‘NNP’, ‘head’: ‘2’, # Note that this is the syntactic head. ‘deprel’: ‘nn’, ‘top’: ‘-‘, ‘pred’: ‘+’, ‘frame’: ‘named:x-c’

Along with a list of arcs and their corresponding tags. Note that in semantic dependency parsing words can have more than one head (it is not a tree), meaning that the list of arcs and tags are not tied to the length of the sentence.