German noun chunk iterator now doesn't return tokens more than once

spacy/symbols.pxd

@@ -382,7 +382,6 @@ cpdef enum symbol_t:
     cc
     ccomp
     complm
-    compound
     conj
     csubj
     csubjpass

spacy/symbols.pyx

@@ -381,7 +381,6 @@ IDS = {
     "cc": cc,
     "ccomp": ccomp,
     "complm": complm,
-    "compound": compound,
     "conj": conj,
     "csubj": csubj,
     "csubjpass": csubjpass,

spacy/syntax/iterators.pyx

@@ -32,7 +32,9 @@ def german_noun_chunks(doc):
     np_deps = set(doc.vocab.strings[label] for label in labels)
     close_app = doc.vocab.strings['nk']
 
-    for word in doc:
+    i = 0
+    while i  < len(doc):
+        word = doc[i]
         if word.pos == NOUN and word.dep in np_deps:
             rbracket = word.i+1
             # try to extend the span to the right
@@ -40,7 +42,9 @@ def german_noun_chunks(doc):
             for rdep in doc[word.i].rights:
                 if rdep.pos == NOUN and rdep.dep == close_app:
                     rbracket = rdep.i+1
-            yield word.l_edge, rbracket, np_label
-
+            yield word.left_edge.i, rbracket, np_label
+            i = rbracket
+            continue
+        i += 1
 
 CHUNKERS = {'en': english_noun_chunks, 'de': german_noun_chunks}

spacy/tests/unit/test_parser.py

@@ -1,10 +1,11 @@
+# -*- coding: utf-8 -*-
+
 from __future__ import unicode_literals
 
 import pytest
 import numpy
 
 from spacy.attrs import HEAD, DEP
-from spacy.symbols import root, det, compound, nsubjpass, aux, auxpass, punct, nsubj, cc, amod, conj, advmod, attr, prep, pobj
 
 
 @pytest.mark.models
@@ -13,6 +14,7 @@ class TestNounChunks:
     def ex1_en(self, EN):
         example = EN.tokenizer.tokens_from_list('A base phrase should be recognized .'.split(' '))
         EN.tagger.tag_from_strings(example, 'DT NN NN MD VB VBN .'.split(' '))
+        det,compound,nsubjpass,aux,auxpass,root,punct = tuple( EN.vocab.strings[l] for l in ['det','compound','nsubjpass','aux','auxpass','root','punct'] )
         example.from_array([HEAD, DEP],
         numpy.asarray(
             [
@@ -30,6 +32,7 @@ class TestNounChunks:
     def ex2_en(self, EN):
         example = EN.tokenizer.tokens_from_list('A base phrase and a good phrase are often the same .'.split(' '))
         EN.tagger.tag_from_strings(example, 'DT NN NN CC DT JJ NN VBP RB DT JJ .'.split(' '))
+        det,compound,nsubj,cc,amod,conj,root,advmod,attr,punct = tuple( EN.vocab.strings[l] for l in ['det','compound','nsubj','cc','amod','conj','root','advmod','attr','punct'] )
         example.from_array([HEAD, DEP],
         numpy.asarray(
             [
@@ -52,6 +55,7 @@ class TestNounChunks:
     def ex3_en(self, EN):
         example = EN.tokenizer.tokens_from_list('A phrase with another phrase occurs .'.split(' '))
         EN.tagger.tag_from_strings(example, 'DT NN IN DT NN VBZ .'.split(' '))
+        det,nsubj,prep,pobj,root,punct = tuple( EN.vocab.strings[l] for l in ['det','nsubj','prep','pobj','root','punct'] )
         example.from_array([HEAD, DEP],
         numpy.asarray(
             [
@@ -65,22 +69,43 @@ class TestNounChunks:
             ], dtype='int32'))
         return example
 
-    # @pytest.fixture(score="class")
-    # def ex1_de(self, DE):
-    #     example = EN.tokenizer.tokens_from_list('Eine Tasse steht auf dem Tisch .'.split(' '))
-    #     EN.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN $.'.split(' '))
-    #     example.from_array([HEAD, DEP],
-    #     numpy.asarray(
-    #         [
-    #             [1, det],
-    #             [4, nsubj],
-    #             [-1, prep],
-    #             [1, det],
-    #             [-2, pobj],
-    #             [0, root],
-    #             [-1, punct]
-    #         ], dtype='int32'))
-    #     return example
+    @pytest.fixture(scope="class")
+    def ex1_de(self, DE):
+        example = DE.tokenizer.tokens_from_list('Eine Tasse steht auf dem Tisch .'.split(' '))
+        DE.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN $.'.split(' '))
+        nk,sb,root,mo,punct = tuple( DE.vocab.strings[l] for l in ['nk','sb','root','mo','punct'])
+        example.from_array([HEAD, DEP],
+        numpy.asarray(
+            [
+                [1, nk],
+                [1, sb],
+                [0, root],
+                [-1, mo],
+                [1, nk],
+                [-2, nk],
+                [-3, punct]
+            ], dtype='int32'))
+        return example
+
+    @pytest.fixture(scope="class")
+    def ex2_de(self, DE):
+        example = DE.tokenizer.tokens_from_list('Die Sängerin singt mit einer Tasse Kaffee Arien .'.split(' '))
+        DE.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN NN NN $.'.split(' '))
+        nk,sb,root,mo,punct,oa = tuple( DE.vocab.strings[l] for l in ['nk','sb','root','mo','punct','oa'])
+        example.from_array([HEAD, DEP],
+        numpy.asarray(
+            [
+                [1, nk],
+                [1, sb],
+                [0, root],
+                [-1, mo],
+                [1, nk],
+                [-2, nk],
+                [-1, nk],
+                [-5, oa],
+                [-6, punct]
+            ], dtype='int32'))
+        return example
 
     def test_en_standard_chunk(self, ex1_en):
         chunks = list(ex1_en.noun_chunks)
@@ -98,3 +123,16 @@ class TestNounChunks:
         assert len(chunks) == 2
         assert chunks[0].string == 'A phrase '
         assert chunks[1].string == 'another phrase '
+
+    def test_de_standard_chunk(self, ex1_de):
+        chunks = list(ex1_de.noun_chunks)
+        assert len(chunks) == 2
+        assert chunks[0].string == 'Eine Tasse '
+        assert chunks[1].string == 'dem Tisch '
+
+    def test_de_extended_chunk(self, ex2_de):
+        chunks = list(ex2_de.noun_chunks)
+        assert len(chunks) == 3
+        assert chunks[0].string == 'Die Sängerin '
+        assert chunks[1].string == 'einer Tasse Kaffee '
+        assert chunks[2].string == 'Arien '