Add (noun chunks) syntax iterators for Danish (#6246)

* add syntax iterators for danish

* add test noun chunks for danish syntax iterators

* add contributor agreement

* update da syntax iterators to remove nested chunks

* add tests for da noun chunks

* Fix test

* add missing import
* fix example

* Prevent overlapping noun chunks

Prevent overlapping noun chunks by tracking the end index of the
previous noun chunk span.

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

.github/contributors/ophelielacroix.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
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+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
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+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
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+
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+
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+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
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+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
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+    against us, our licensees or transferees;
+
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+
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+5. You covenant, represent, warrant and agree that:
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+    * Each contribution that you submit is and shall be an original work of
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+    * each contribution shall be in compliance with U.S. export control laws and
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+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [X] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect to my
+    contributions.
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+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                         | Entry           |
+|-------------------------------|-----------------|
+| Name                          | Ophélie Lacroix |
+| Company name (if applicable)  |                 |
+| Title or role (if applicable) |                 |
+| Date                          |                 |
+| GitHub username               | ophelielacroix  |
+| Website (optional)            |                 |

spacy/lang/da/__init__.py

@@ -7,6 +7,7 @@ from .stop_words import STOP_WORDS
 from .lex_attrs import LEX_ATTRS
 from .morph_rules import MORPH_RULES
 from ..tag_map import TAG_MAP
+from .syntax_iterators import SYNTAX_ITERATORS
 
 from ..tokenizer_exceptions import BASE_EXCEPTIONS
 from ...language import Language
@@ -24,6 +25,7 @@ class DanishDefaults(Language.Defaults):
     suffixes = TOKENIZER_SUFFIXES
     tag_map = TAG_MAP
     stop_words = STOP_WORDS
+    syntax_iterators = SYNTAX_ITERATORS
 
 
 class Danish(Language):

spacy/lang/da/syntax_iterators.py

@@ -0,0 +1,81 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from ...symbols import NOUN, PROPN, PRON, VERB, AUX
+from ...errors import Errors
+
+
+def noun_chunks(doclike):
+    def is_verb_token(tok):
+        return tok.pos in [VERB, AUX]
+
+    def next_token(tok):
+        try:
+            return tok.nbor()
+        except IndexError:
+            return None
+
+    def get_left_bound(doc, root):
+        left_bound = root
+        for tok in reversed(list(root.lefts)):
+            if tok.dep in np_left_deps:
+                left_bound = tok
+        return left_bound
+
+    def get_right_bound(doc, root):
+        right_bound = root
+        for tok in root.rights:
+            if tok.dep in np_right_deps:
+                right = get_right_bound(doc, tok)
+                if list(
+                    filter(
+                        lambda t: is_verb_token(t) or t.dep in stop_deps,
+                        doc[root.i : right.i],
+                    )
+                ):
+                    break
+                else:
+                    right_bound = right
+        return right_bound
+
+    def get_bounds(doc, root):
+        return get_left_bound(doc, root), get_right_bound(doc, root)
+
+    doc = doclike.doc
+
+    if not doc.is_parsed:
+        raise ValueError(Errors.E029)
+
+    if not len(doc):
+        return
+
+    left_labels = [
+        "det",
+        "fixed",
+        "nmod:poss",
+        "amod",
+        "flat",
+        "goeswith",
+        "nummod",
+        "appos",
+    ]
+    right_labels = ["fixed", "nmod:poss", "amod", "flat", "goeswith", "nummod", "appos"]
+    stop_labels = ["punct"]
+
+    np_label = doc.vocab.strings.add("NP")
+    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]
+
+    chunks = []
+    prev_right = -1
+    for token in doclike:
+        if token.pos in [PROPN, NOUN, PRON]:
+            left, right = get_bounds(doc, token)
+            if left.i <= prev_right:
+                continue
+            yield left.i, right.i + 1, np_label
+            prev_right = right.i
+
+
+SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

spacy/tests/lang/da/test_noun_chunks.py

@@ -0,0 +1,61 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+from ...util import get_doc
+
+
+def test_noun_chunks_is_parsed(da_tokenizer):
+    """Test that noun_chunks raises Value Error for 'da' language if Doc is not parsed.
+    To check this test, we're constructing a Doc
+    with a new Vocab here and forcing is_parsed to 'False'
+    to make sure the noun chunks don't run.
+    """
+    doc = da_tokenizer("Det er en sætning")
+    doc.is_parsed = False
+    with pytest.raises(ValueError):
+        list(doc.noun_chunks)
+
+
+
+DA_NP_TEST_EXAMPLES = [
+    (
+        "Hun elsker at plukker frugt.",
+        ['PRON', 'VERB', 'PART', 'VERB', 'NOUN', 'PUNCT'],
+        ['nsubj', 'ROOT', 'mark', 'obj', 'obj', 'punct'],
+        [1, 0, 1, -2, -1, -4],
+        ["Hun", "frugt"],
+    ),
+    (
+        "Påfugle er de smukkeste fugle.",
+        ['NOUN', 'AUX', 'DET', 'ADJ', 'NOUN', 'PUNCT'],
+        ['nsubj', 'cop', 'det', 'amod', 'ROOT', 'punct'],
+        [4, 3, 2, 1, 0, -1],
+        ["Påfugle", "de smukkeste fugle"],
+    ),
+    (
+        "Rikke og Jacob Jensen glæder sig til en hyggelig skovtur",
+        ['PROPN', 'CCONJ', 'PROPN', 'PROPN', 'VERB', 'PRON', 'ADP', 'DET', 'ADJ', 'NOUN'],
+        ['nsubj', 'cc', 'conj', 'flat', 'ROOT', 'obj', 'case', 'det', 'amod', 'obl'],
+        [4, 1, -2, -1, 0, -1, 3, 2, 1, -5],
+        ["Rikke", "Jacob Jensen", "sig", "en hyggelig skovtur"],
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "text,pos,deps,heads,expected_noun_chunks", DA_NP_TEST_EXAMPLES
+)
+def test_da_noun_chunks(da_tokenizer, text, pos, deps, heads, expected_noun_chunks):
+    tokens = da_tokenizer(text)
+
+    assert len(heads) == len(pos)
+    doc = get_doc(
+        tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps, pos=pos
+    )
+
+    noun_chunks = list(doc.noun_chunks)
+    assert len(noun_chunks) == len(expected_noun_chunks)
+    for i, np in enumerate(noun_chunks):
+        assert np.text == expected_noun_chunks[i]