| from oie_readers.oieReader import OieReader |
| from oie_readers.extraction import Extraction |
|
|
|
|
| class PropSReader(OieReader): |
| |
| def __init__(self): |
| self.name = 'PropS' |
| |
| def read(self, fn): |
| d = {} |
| with open(fn) as fin: |
| for line in fin: |
| if not line.strip(): |
| continue |
| data = line.strip().split('\t') |
| confidence, text, rel = data[:3] |
| curExtraction = Extraction(pred = rel, sent = text, confidence = float(confidence), head_pred_index=-1) |
| |
| for arg in data[4::2]: |
| curExtraction.addArg(arg) |
| |
| d[text] = d.get(text, []) + [curExtraction] |
| self.oie = d |
| |
| |
| |
| def normalizeConfidence(self): |
| ''' Normalize confidence to resemble probabilities ''' |
| EPSILON = 1e-3 |
| |
| self.confidences = [extraction.confidence for sent in self.oie for extraction in self.oie[sent]] |
| maxConfidence = max(self.confidences) |
| minConfidence = min(self.confidences) |
| |
| denom = maxConfidence - minConfidence + (2*EPSILON) |
| |
| for sent, extractions in self.oie.items(): |
| for extraction in extractions: |
| extraction.confidence = ( (extraction.confidence - minConfidence) + EPSILON) / denom |
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|
