      filtered = []
      for raw_text, sents in gold_tuples:
          filtered_sents = []
          for (ids, words, tags, heads, labels, iob), ctnts in sents:
              filtered_labels = [decompose(label)[0]
                                 if freqs.get(label, cutoff) < cutoff
                                 else label for label in labels]
              filtered_sents.append(
                  ((ids, words, tags, heads, filtered_labels, iob), ctnts))
          filtered.append((raw_text, filtered_sents))
      return filtered
 
</s> remove     return label.find(DELIMITER) != -1
 </s> add     return DELIMITER in label

def count_decorated_labels(gold_tuples):
    freqs = {}
    for raw_text, sents in gold_tuples:
        for (ids, words, tags, heads, labels, iob), ctnts in sents:
            proj_heads, deco_labels = projectivize(heads, labels)
            # set the label to ROOT for each root dependent
            deco_labels = ['ROOT' if head == i else deco_labels[i]
                           for i, head in enumerate(proj_heads)]
            # count label frequencies
            for label in deco_labels:
                if is_decorated(label):
                    freqs[label] = freqs.get(label, 0) + 1
    return freqs </s> remove             if entity_type in seen_entities:
                continue
            seen_entities.add(entity_type)
 </s> add  </s> remove                 actions[action].append(entity_type)
 </s> add                 actions[action][entity_type] = 1 </s> remove         self.cand_to_gold = align([t.orth_ for t in doc], words)
        self.gold_to_cand = align(words, [t.orth_ for t in doc])
 </s> add         # This needs to be done before we align the words
        if make_projective and heads is not None and deps is not None:
            heads, deps = nonproj.projectivize(heads, deps)

        # Do many-to-one alignment for misaligned tokens.
        # If we over-segment, we'll have one gold word that covers a sequence
        # of predicted words
        # If we under-segment, we'll have one predicted word that covers a
        # sequence of gold words.
        # If we "mis-segment", we'll have a sequence of predicted words covering
        # a sequence of gold words. That's many-to-many -- we don't do that.
        cost, i2j, j2i, i2j_multi, j2i_multi = align([t.orth_ for t in doc], words)

        self.cand_to_gold = [(j if j >= 0 else None) for j in i2j]
        self.gold_to_cand = [(i if i >= 0 else None) for i in j2i] </s> remove     np_left_deps = [doc.vocab.strings[label] for label in left_labels]
    np_right_deps = [doc.vocab.strings[label] for label in right_labels]
    stop_deps = [doc.vocab.strings[label] for label in stop_labels]
 </s> add     np_left_deps = [doc.vocab.strings.add(label) for label in left_labels]
    np_right_deps = [doc.vocab.strings.add(label) for label in right_labels]
    stop_deps = [doc.vocab.strings.add(label) for label in stop_labels]
            filtered_labels = []
            for label in labels:
                if is_decorated(label) and freqs.get(label, 0) < cutoff:
                    filtered_labels.append(decompose(label)[0])
                else:
                    filtered_labels.append(label)
      filtered = []
      for raw_text, sents in gold_tuples:
          filtered_sents = []
          for (ids, words, tags, heads, labels, iob), ctnts in sents:
            filtered_labels = []
            for label in labels:
                if is_decorated(label) and freqs.get(label, 0) < cutoff:
                    filtered_labels.append(decompose(label)[0])
                else:
                    filtered_labels.append(label)
            filtered_labels = []
            for label in labels:
                if is_decorated(label) and freqs.get(label, 0) < cutoff:
                    filtered_labels.append(decompose(label)[0])
                else:
                    filtered_labels.append(label)
            filtered_labels = []
            for label in labels:
                if is_decorated(label) and freqs.get(label, 0) < cutoff:
                    filtered_labels.append(decompose(label)[0])
                else:
                    filtered_labels.append(label)
              filtered_sents.append(
                  ((ids, words, tags, heads, filtered_labels, iob), ctnts))
          filtered.append((raw_text, filtered_sents))
      return filtered
keep keep keep keep replace replace replace keep keep keep keep
 Revert "Merge branch 'develop' of https://github.com/explosion/spaCy into develop"

This reverts commit c9ba3d3c2dc7067cf8bd55f878cec45a8c6d73d4, reversing
changes made to 92c26a35d425d4e8ca1b805ea776ea10f5ded3df. 
spacy/syntax/nonproj.pyx