diff --git a/examples/training/train_intent_parser.py b/examples/training/train_intent_parser.py
new file mode 100644
index 000000000..e67f26aff
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+++ b/examples/training/train_intent_parser.py
@@ -0,0 +1,157 @@
+#!/usr/bin/env python
+# coding: utf-8
+"""Using the parser to recognise your own semantics spaCy's parser component
+can be used to trained to predict any type of tree structure over your input
+text. You can also predict trees over whole documents or chat logs, with
+connections between the sentence-roots used to annotate discourse structure.
+
+In this example, we'll build a message parser for a common "chat intent":
+finding local businesses. Our message semantics will have the following types
+of relations: INTENT, PLACE, QUALITY, ATTRIBUTE, TIME, LOCATION. For example:
+
+"show me the best hotel in berlin"
+('show', 'ROOT', 'show')
+('best', 'QUALITY', 'hotel') --> hotel with QUALITY best
+('hotel', 'PLACE', 'show') --> show PLACE hotel
+('berlin', 'LOCATION', 'hotel') --> hotel with LOCATION berlin
+"""
+from __future__ import unicode_literals, print_function
+
+import plac
+import random
+import spacy
+from spacy.gold import GoldParse
+from spacy.tokens import Doc
+from pathlib import Path
+
+
+# training data: words, head and dependency labels
+# for no relation, we simply chose an arbitrary dependency label, e.g. '-'
+TRAIN_DATA = [
+    (
+        ['find', 'a', 'cafe', 'with', 'great', 'wifi'],
+        [0, 2, 0, 5, 5, 2],  # index of token head
+        ['ROOT', '-', 'PLACE', '-', 'QUALITY', 'ATTRIBUTE']
+    ),
+    (
+        ['find', 'a', 'hotel', 'near', 'the', 'beach'],
+        [0, 2, 0, 5, 5, 2],
+        ['ROOT', '-', 'PLACE', 'QUALITY', '-', 'ATTRIBUTE']
+    ),
+    (
+        ['find', 'me', 'the', 'closest', 'gym', 'that', "'s", 'open', 'late'],
+        [0, 0, 4, 4, 0, 6, 4, 6, 6],
+        ['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', '-', 'ATTRIBUTE', 'TIME']
+    ),
+    (
+        ['show', 'me', 'the', 'cheapest', 'store', 'that', 'sells', 'flowers'],
+        [0, 0, 4, 4, 0, 4, 4, 4],  # attach "flowers" to store!
+        ['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', '-', 'PRODUCT']
+    ),
+    (
+        ['find', 'a', 'nice', 'restaurant', 'in', 'london'],
+        [0, 3, 3, 0, 3, 3],
+        ['ROOT', '-', 'QUALITY', 'PLACE', '-', 'LOCATION']
+    ),
+    (
+        ['show', 'me', 'the', 'coolest', 'hostel', 'in', 'berlin'],
+        [0, 0, 4, 4, 0, 4, 4],
+        ['ROOT', '-', '-', 'QUALITY', 'PLACE', '-', 'LOCATION']
+    ),
+    (
+        ['find', 'a', 'good', 'italian', 'restaurant', 'near', 'work'],
+        [0, 4, 4, 4, 0, 4, 5],
+        ['ROOT', '-', 'QUALITY', 'ATTRIBUTE', 'PLACE', 'ATTRIBUTE', 'LOCATION']
+    )
+]
+
+
+@plac.annotations(
+    model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
+    output_dir=("Optional output directory", "option", "o", Path),
+    n_iter=("Number of training iterations", "option", "n", int))
+def main(model=None, output_dir=None, n_iter=100):
+    """Load the model, set up the pipeline and train the parser."""
+    if model is not None:
+        nlp = spacy.load(model)  # load existing spaCy model
+        print("Loaded model '%s'" % model)
+    else:
+        nlp = spacy.blank('en')  # create blank Language class
+        print("Created blank 'en' model")
+
+    # add the parser to the pipeline if it doesn't exist
+    # nlp.create_pipe works for built-ins that are registered with spaCy
+    if 'parser' not in nlp.pipe_names:
+        parser = nlp.create_pipe('parser')
+        nlp.add_pipe(parser, first=True)
+    # otherwise, get it, so we can add labels to it
+    else:
+        parser = nlp.get_pipe('parser')
+
+    for _, _, deps in TRAIN_DATA:
+        for dep in deps:
+            parser.add_label(dep)
+
+    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
+    with nlp.disable_pipes(*other_pipes):  # only train parser
+        optimizer = nlp.begin_training(lambda: [])
+        for itn in range(n_iter):
+            random.shuffle(TRAIN_DATA)
+            losses = {}
+            for words, heads, deps in TRAIN_DATA:
+                doc = Doc(nlp.vocab, words=words)
+                gold = GoldParse(doc, heads=heads, deps=deps)
+                nlp.update([doc], [gold], sgd=optimizer, losses=losses)
+            print(losses)
+
+    # test the trained model
+    test_model(nlp)
+
+    # save model to output directory
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        if not output_dir.exists():
+            output_dir.mkdir()
+        nlp.to_disk(output_dir)
+        print("Saved model to", output_dir)
+
+        # test the saved model
+        print("Loading from", output_dir)
+        nlp2 = spacy.load(output_dir)
+        test_model(nlp2)
+
+
+def test_model(nlp):
+    texts = ["find a hotel with good wifi",
+             "find me the cheapest gym near work",
+             "show me the best hotel in berlin"]
+    docs = nlp.pipe(texts)
+    for doc in docs:
+        print(doc.text)
+        print([(t.text, t.dep_, t.head.text) for t in doc if t.dep_ != '-'])
+
+
+if __name__ == '__main__':
+    plac.call(main)
+
+    # Expected output:
+    # find a hotel with good wifi
+    # [
+    #   ('find', 'ROOT', 'find'),
+    #   ('hotel', 'PLACE', 'find'),
+    #   ('good', 'QUALITY', 'wifi'),
+    #   ('wifi', 'ATTRIBUTE', 'hotel')
+    # ]
+    # find me the cheapest gym near work
+    # [
+    #   ('find', 'ROOT', 'find'),
+    #   ('cheapest', 'QUALITY', 'gym'),
+    #   ('gym', 'PLACE', 'find')
+    # ]
+    # show me the best hotel in berlin
+    # [
+    #   ('show', 'ROOT', 'show'),
+    #   ('best', 'QUALITY', 'hotel'),
+    #   ('hotel', 'PLACE', 'show'),
+    #   ('berlin', 'LOCATION', 'hotel')
+    # ]