framenet_tools.fee_identification package¶

Submodules¶

class framenet_tools.fee_identification.feeidentifier.FeeIdentifier(cM: framenet_tools.config.ConfigManager)¶

Bases: object

evaluate_acc(dataset: List[List[str]])¶

Evaluates the accuracy of the Frame Evoking Element Identifier

NOTE: F1-Score is a better way to evaluate the Identifier, because it tends to predict too many FEEs

Parameters:	dataset – The dataset to evaluate
Returns:	A Triple of the count of correct elements, total elements and the accuracy

identify_targets(sentence: list)¶

Identifies targets for a given sentence

Parameters:	sentence – A list of words in a sentence
Returns:	A list of targets

predict_fees(mReader: framenet_tools.data_handler.reader.DataReader)¶

Predicts the Frame Evoking Elements NOTE: This drops current annotation data

Returns:

predict_fees_old(dataset: List[List[str]])¶

Predicts all FEEs for a complete datset

Parameters:	dataset – The dataset to predict
Returns:	A list of predictions

query(x: List[str])¶

Query a prediction of FEEs for a given sentence

Parameters:	x – A list of words in a sentence
Returns:	A list of predicted FEEs

framenet_tools.fee_identification.feeidentifier.should_include_token(p_data: list)¶

A static syntactical prediction of possible Frame Evoking Elements

Parameters:	p_data – A list of lists containing token, pos_tag, lemma and NE
Returns:	A list of possible FEEs