diff --git a/spacy/syntax/nonproj.pyx b/spacy/syntax/nonproj.pyx
index ee3219392..5b1f57d2b 100644
--- a/spacy/syntax/nonproj.pyx
+++ b/spacy/syntax/nonproj.pyx
@@ -7,7 +7,7 @@ from copy import copy
 
 from ..tokens.doc cimport Doc, set_children_from_heads
 
-from ..gold import Example, TokenAnnotation
+from ..gold import Example
 from ..errors import Errors
 
 
@@ -90,31 +90,6 @@ def count_decorated_labels(gold_data):
     return freqs
 
 
-def preprocess_training_data(gold_data, label_freq_cutoff=30):
-    preprocessed = []
-    freqs = {}
-    for example in gold_data:
-        new_example = Example(doc=example.doc)
-        proj_heads, deco_deps = projectivize(example.token_annotation.heads,
-                                             example.token_annotation.deps)
-        # set the label to ROOT for each root dependent
-        deco_deps = ['ROOT' if head == i else deco_deps[i]
-                       for i, head in enumerate(proj_heads)]
-        # count label frequencies
-        if label_freq_cutoff > 0:
-            for label in deco_deps:
-                if is_decorated(label):
-                    freqs[label] = freqs.get(label, 0) + 1
-        proj_token_dict = example.token_annotation.to_dict()
-        proj_token_dict["heads"] = proj_heads
-        proj_token_dict["deps"] = deco_deps
-        new_example.token_annotation = TokenAnnotation(**proj_token_dict)
-        preprocessed.append(new_example)
-    if label_freq_cutoff > 0:
-        return _filter_labels(preprocessed, label_freq_cutoff, freqs)
-    return preprocessed
-
-
 def projectivize(heads, labels):
     # Use the algorithm by Nivre & Nilsson 2005. Assumes heads to be a proper
     # tree, i.e. connected and cycle-free. Returns a new pair (heads, labels)
@@ -200,22 +175,3 @@ def _find_new_head(token, headlabel):
                 next_queue.append(child)
         queue = next_queue
     return token.head
-
-
-def _filter_labels(examples, cutoff, freqs):
-    # throw away infrequent decorated labels
-    # can't learn them reliably anyway and keeps label set smaller
-    filtered = []
-    for example in examples:
-        new_example = Example(doc=example.doc)
-        filtered_labels = []
-        for label in example.token_annotation.deps:
-            if is_decorated(label) and freqs.get(label, 0) < cutoff:
-                filtered_labels.append(decompose(label)[0])
-            else:
-                filtered_labels.append(label)
-        filtered_token_dict = example.token_annotation.to_dict()
-        filtered_token_dict["deps"] = filtered_labels
-        new_example.token_annotation = TokenAnnotation(**filtered_token_dict)
-        filtered.append(new_example)
-    return filtered