我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用utils.read_conll()。
def Predict(self, conll_path): with open(conll_path, 'r') as conllFP: for iSentence, sentence in enumerate(read_conll(conllFP, False)): self.Init() forest = ParseForest(sentence) self.getWordEmbeddings(forest, False) for root in forest.roots: root.lstms = [self.builders[0].initial_state().add_input(root.vec), self.builders[1].initial_state().add_input(root.vec)] while len(forest.roots) > 1: self.__evaluate(forest, False) bestParent, bestChild, bestScore = None, None, float("-inf") bestIndex, bestOp = None, None roots = forest.roots for i in xrange(len(forest.roots) - 1): for irel, rel in enumerate(self.irels): for op in xrange(2): if bestScore < roots[i].scores[irel][op] and (i + (1 - op)) > 0: bestParent, bestChild = i + op, i + (1 - op) bestScore = roots[i].scores[irel][op] bestIndex, bestOp = i, op bestRelation, bestIRelation = rel, irel for j in xrange(max(0, bestIndex - self.k - 1), min(len(forest.roots), bestIndex + self.k + 2)): roots[j].scores = None roots[bestChild].pred_parent_id = forest.roots[bestParent].id roots[bestChild].pred_relation = bestRelation roots[bestParent].lstms[bestOp] = roots[bestParent].lstms[bestOp].add_input((self.activation(self.lstm2lstmbias + self.lstm2lstm * concatenate([roots[bestChild].lstms[0].output(), lookup(self.model["rels-lookup"], bestIRelation), roots[bestChild].lstms[1].output()])))) forest.Attach(bestParent, bestChild) renew_cg() yield sentence
def Predict(self, conll_path): with open(conll_path, 'r') as conllFP: for iSentence, sentence in enumerate(read_conll(conllFP, False)): self.Init() conll_sentence = [entry for entry in sentence if isinstance(entry, utils.ConllEntry)] conll_sentence = conll_sentence[1:] + [conll_sentence[0]] self.getWordEmbeddings(conll_sentence, False) stack = ParseForest([]) buf = ParseForest(conll_sentence) for root in conll_sentence: root.lstms = [root.vec for _ in xrange(self.nnvecs)] hoffset = 1 if self.headFlag else 0 while not (len(buf) == 1 and len(stack) == 0): scores = self.__evaluate(stack, buf, False) best = max(chain(*scores), key = itemgetter(2) ) if best[1] == 2: stack.roots.append(buf.roots[0]) del buf.roots[0] elif best[1] == 0: child = stack.roots.pop() parent = buf.roots[0] child.pred_parent_id = parent.id child.pred_relation = best[0] bestOp = 0 if self.rlMostFlag: parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset] if self.rlFlag: parent.lstms[bestOp + hoffset] = child.vec elif best[1] == 1: child = stack.roots.pop() parent = stack.roots[-1] child.pred_parent_id = parent.id child.pred_relation = best[0] bestOp = 1 if self.rlMostFlag: parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset] if self.rlFlag: parent.lstms[bestOp + hoffset] = child.vec renew_cg() yield sentence
def Predict(self, conll_path): with open(conll_path, 'r') as conllFP: for iSentence, sentence in enumerate(read_conll(conllFP)): conll_sentence = [entry for entry in sentence if isinstance(entry, utils.ConllEntry)] for entry in conll_sentence: wordvec = self.wlookup[int(self.vocab.get(entry.norm, 0))] if self.wdims > 0 else None posvec = self.plookup[int(self.pos[entry.pos])] if self.pdims > 0 else None evec = self.elookup[int(self.extrnd.get(entry.form, self.extrnd.get(entry.norm, 0)))] if self.external_embedding is not None else None entry.vec = concatenate(filter(None, [wordvec, posvec, evec])) entry.lstms = [entry.vec, entry.vec] entry.headfov = None entry.modfov = None entry.rheadfov = None entry.rmodfov = None if self.blstmFlag: lstm_forward = self.builders[0].initial_state() lstm_backward = self.builders[1].initial_state() for entry, rentry in zip(conll_sentence, reversed(conll_sentence)): lstm_forward = lstm_forward.add_input(entry.vec) lstm_backward = lstm_backward.add_input(rentry.vec) entry.lstms[1] = lstm_forward.output() rentry.lstms[0] = lstm_backward.output() if self.bibiFlag: for entry in conll_sentence: entry.vec = concatenate(entry.lstms) blstm_forward = self.bbuilders[0].initial_state() blstm_backward = self.bbuilders[1].initial_state() for entry, rentry in zip(conll_sentence, reversed(conll_sentence)): blstm_forward = blstm_forward.add_input(entry.vec) blstm_backward = blstm_backward.add_input(rentry.vec) entry.lstms[1] = blstm_forward.output() rentry.lstms[0] = blstm_backward.output() scores, exprs = self.__evaluate(conll_sentence, True) heads = decoder.parse_proj(scores) for entry, head in zip(conll_sentence, heads): entry.pred_parent_id = head entry.pred_relation = '_' dump = False if self.labelsFlag: for modifier, head in enumerate(heads[1:]): scores, exprs = self.__evaluateLabel(conll_sentence, head, modifier+1) conll_sentence[modifier+1].pred_relation = self.irels[max(enumerate(scores), key=itemgetter(1))[0]] renew_cg() if not dump: yield sentence
def Predict(self, conll_path): with open(conll_path, 'r') as conllFP: for iSentence, sentence in enumerate(read_conll(conllFP, False)): self.Init() sentence = sentence[1:] + [sentence[0]] self.getWordEmbeddings(sentence, False) stack = ParseForest([]) buf = ParseForest(sentence) for root in sentence: root.lstms = [root.vec for _ in xrange(self.nnvecs)] hoffset = 1 if self.headFlag else 0 while len(buf) > 0 or len(stack) > 1 : scores = self.__evaluate(stack, buf, False) best = max(chain(*scores), key = itemgetter(2) ) if best[1] == 2: stack.roots.append(buf.roots[0]) del buf.roots[0] elif best[1] == 0: child = stack.roots.pop() parent = buf.roots[0] child.pred_parent_id = parent.id child.pred_relation = best[0] bestOp = 0 if self.rlMostFlag: parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset] if self.rlFlag: parent.lstms[bestOp + hoffset] = child.vec elif best[1] == 1: child = stack.roots.pop() parent = stack.roots[-1] child.pred_parent_id = parent.id child.pred_relation = best[0] bestOp = 1 if self.rlMostFlag: parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset] if self.rlFlag: parent.lstms[bestOp + hoffset] = child.vec renew_cg() yield [sentence[-1]] + sentence[:-1]
