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106
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# 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
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"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
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
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;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
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.
* [ ] 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 | Matthew Chin |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-09-22 |
| GitHub username | delzac |
| Website (optional) | |

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# 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
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"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
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
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;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
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.
* [ ] 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 | Florijan Stamenkovic |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-10-05 |
| GitHub username | florijanstamenkovic |
| Website (optional) | |

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# 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
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"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
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
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;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
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.
* [ ] 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 | Šarūnas Navickas |
| Company name (if applicable) | TokenMill |
| Title or role (if applicable) | Data Engineer |
| Date | 2020-09-24 |
| GitHub username | zaibacu |
| Website (optional) | |

2
spacy/lang/tr/__init__.py
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@ -1,5 +1,6 @@
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .lex_attrs import LEX_ATTRS
from ...language import Language
@ -8,6 +9,7 @@ class TurkishDefaults(Language.Defaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
syntax_iterators = SYNTAX_ITERATORS
class Turkish(Language):

44
spacy/lang/tr/lex_attrs.py
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@ -32,6 +32,36 @@ _num_words = [
]
_ordinal_words = [
"birinci",
"ikinci",
"üçüncü",
"dördüncü",
"beşinci",
"altıncı",
"yedinci",
"sekizinci",
"dokuzuncu",
"onuncu",
"yirminci",
"otuzuncu",
"kırkıncı",
"ellinci",
"altmışıncı",
"yetmişinci",
"sekseninci",
"doksanıncı",
"yüzüncü",
"bininci",
"mliyonuncu",
"milyarıncı",
"trilyonuncu",
"katrilyonuncu",
"kentilyonuncu",
]
_ordinal_endings = ("inci", "ıncı", "nci", "ncı", "uncu", "üncü")
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
@ -42,8 +72,20 @@ def like_num(text):
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
text_lower = text.lower()
#Check cardinal number
if text_lower in _num_words:
return True
#Check ordinal number
if text_lower in _ordinal_words:
return True
if text_lower.endswith(_ordinal_endings):
if text_lower[:-3].isdigit() or text_lower[:-4].isdigit():
return True
return False

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spacy/lang/tr/syntax_iterators.py Normal file
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@ -0,0 +1,59 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import NOUN, PROPN, PRON
from ...errors import Errors
def noun_chunks(doclike):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
# Please see documentation for Turkish NP structure
labels = [
"nsubj",
"iobj",
"obj",
"obl",
"appos",
"orphan",
"dislocated",
"ROOT",
]
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
np_deps = [doc.vocab.strings.add(label) for label in labels]
conj = doc.vocab.strings.add("conj")
flat = doc.vocab.strings.add("flat")
np_label = doc.vocab.strings.add("NP")
def extend_right(w): # Playing a trick for flat
rindex = w.i + 1
for rdep in doc[w.i].rights: # Extend the span to right if there is a flat
if rdep.dep == flat and rdep.pos in (NOUN, PROPN):
rindex = rdep.i + 1
else:
break
return rindex
prev_end = len(doc) + 1
for i, word in reversed(list(enumerate(doclike))):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i >= prev_end:
continue
if word.dep in np_deps:
prev_end = word.left_edge.i
yield word.left_edge.i, extend_right(word), np_label
elif word.dep == conj:
cc_token = word.left_edge
prev_end = cc_token.i
yield cc_token.right_edge.i + 1, extend_right(word), np_label # Shave off cc tokens from the NP
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

3
spacy/tests/conftest.py
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@ -239,6 +239,9 @@ def th_tokenizer():
def tr_tokenizer():
return get_lang_class("tr")().tokenizer
@pytest.fixture(scope="session")
def tr_vocab():
return get_lang_class("tr").Defaults.create_vocab()
@pytest.fixture(scope="session")
def tt_tokenizer():

12
spacy/tests/lang/tr/test_noun_chunks.py Normal file
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@ -0,0 +1,12 @@
import pytest
def test_noun_chunks_is_parsed(tr_tokenizer):
"""Test that noun_chunks raises Value Error for 'tr' 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 = tr_tokenizer("Dün seni gördüm.")
with pytest.raises(ValueError):
list(doc.noun_chunks)

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spacy/tests/lang/tr/test_parser.py Normal file
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@ -0,0 +1,570 @@
from spacy.tokens import Doc
def test_tr_noun_chunks_amod_simple(tr_tokenizer):
text = "sarı kedi"
heads = [1, 1]
deps = ["amod", "ROOT"]
pos = ["ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "sarı kedi "
def test_tr_noun_chunks_nmod_simple(tr_tokenizer):
text = "arkadaşımın kedisi" # my friend's cat
heads = [1, 1]
deps = ["nmod", "ROOT"]
pos = ["NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "arkadaşımın kedisi "
def test_tr_noun_chunks_determiner_simple(tr_tokenizer):
text = "O kedi" # that cat
heads = [1, 1]
deps = ["det", "ROOT"]
pos = ["DET", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "O kedi "
def test_tr_noun_chunks_nmod_amod(tr_tokenizer):
text = "okulun eski müdürü"
heads = [2, 2, 2]
deps = ["nmod", "amod", "ROOT"]
pos = ["NOUN", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "okulun eski müdürü "
def test_tr_noun_chunks_one_det_one_adj_simple(tr_tokenizer):
text = "O sarı kedi"
heads = [2, 2, 2]
deps = ["det", "amod", "ROOT"]
pos = ["DET", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "O sarı kedi "
def test_tr_noun_chunks_two_adjs_simple(tr_tokenizer):
text = "beyaz tombik kedi"
heads = [2, 2, 2]
deps = ["amod", "amod", "ROOT"]
pos = ["ADJ", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "beyaz tombik kedi "
def test_tr_noun_chunks_one_det_two_adjs_simple(tr_tokenizer):
text = "o beyaz tombik kedi"
heads = [3, 3, 3, 3]
deps = ["det", "amod", "amod", "ROOT"]
pos = ["DET", "ADJ", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "o beyaz tombik kedi "
def test_tr_noun_chunks_nmod_two(tr_tokenizer):
text = "kızın saçının rengi"
heads = [1, 2, 2]
deps = ["nmod", "nmod", "ROOT"]
pos = ["NOUN", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "kızın saçının rengi "
def test_tr_noun_chunks_chain_nmod_with_adj(tr_tokenizer):
text = "ev sahibinin tatlı köpeği"
heads = [1, 3, 3, 3]
deps = ["nmod", "nmod", "amod", "ROOT"]
pos = ["NOUN", "NOUN", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "ev sahibinin tatlı köpeği "
def test_tr_noun_chunks_chain_nmod_with_acl(tr_tokenizer):
text = "ev sahibinin gelen köpeği"
heads = [1, 3, 3, 3]
deps = ["nmod", "nmod", "acl", "ROOT"]
pos = ["NOUN", "NOUN", "VERB", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "ev sahibinin gelen köpeği "
def test_tr_noun_chunks_chain_nmod_head_with_amod_acl(tr_tokenizer):
text = "arabanın kırdığım sol aynası"
heads = [3, 3, 3, 3]
deps = ["nmod", "acl", "amod", "ROOT"]
pos = ["NOUN", "VERB", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "arabanın kırdığım sol aynası "
def test_tr_noun_chunks_nmod_three(tr_tokenizer):
text = "güney Afrika ülkelerinden Mozambik"
heads = [1, 2, 3, 3]
deps = ["nmod", "nmod", "nmod", "ROOT"]
pos = ["NOUN", "PROPN", "NOUN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "güney Afrika ülkelerinden Mozambik "
def test_tr_noun_chunks_det_amod_nmod(tr_tokenizer):
text = "bazı eski oyun kuralları"
heads = [3, 3, 3, 3]
deps = ["det", "nmod", "nmod", "ROOT"]
pos = ["DET", "ADJ", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "bazı eski oyun kuralları "
def test_tr_noun_chunks_acl_simple(tr_tokenizer):
text = "bahçesi olan okul"
heads = [2, 0, 2]
deps = ["acl", "cop", "ROOT"]
pos = ["NOUN", "AUX", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "bahçesi olan okul "
def test_tr_noun_chunks_acl_verb(tr_tokenizer):
text = "sevdiğim sanatçılar"
heads = [1, 1]
deps = ["acl", "ROOT"]
pos = ["VERB", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "sevdiğim sanatçılar "
def test_tr_noun_chunks_acl_nmod(tr_tokenizer):
text = "en sevdiğim ses sanatçısı"
heads = [1, 3, 3, 3]
deps = ["advmod", "acl", "nmod", "ROOT"]
pos = ["ADV", "VERB", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "en sevdiğim ses sanatçısı "
def test_tr_noun_chunks_acl_nmod(tr_tokenizer):
text = "bildiğim bir turizm şirketi"
heads = [3, 3, 3, 3]
deps = ["acl", "det", "nmod", "ROOT"]
pos = ["VERB", "DET", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "bildiğim bir turizm şirketi "
def test_tr_noun_chunks_np_recursive_nsubj_to_root(tr_tokenizer):
text = "Simge'nin okuduğu kitap"
heads = [1, 2, 2]
deps = ["nsubj", "acl", "ROOT"]
pos = ["PROPN", "VERB", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Simge'nin okuduğu kitap "
def test_tr_noun_chunks_np_recursive_nsubj_attached_to_pron_root(tr_tokenizer):
text = "Simge'nin konuşabileceği birisi"
heads = [1, 2, 2]
deps = ["nsubj", "acl", "ROOT"]
pos = ["PROPN", "VERB", "PRON"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Simge'nin konuşabileceği birisi "
def test_tr_noun_chunks_np_recursive_nsubj_in_subnp(tr_tokenizer):
text = "Simge'nin yarın gideceği yer"
heads = [2, 2, 3, 3]
deps = ["nsubj", "obl", "acl", "ROOT"]
pos = ["PROPN", "NOUN", "VERB", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Simge'nin yarın gideceği yer "
def test_tr_noun_chunks_np_recursive_two_nmods(tr_tokenizer):
text = "ustanın kapısını degiştireceği çamasır makinası"
heads = [2, 2, 4, 4, 4]
deps = ["nsubj", "obj", "acl", "nmod", "ROOT"]
pos = ["NOUN", "NOUN", "VERB", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "ustanın kapısını degiştireceği çamasır makinası "
def test_tr_noun_chunks_np_recursive_four_nouns(tr_tokenizer):
text = "kızına piyano dersi verdiğim hanım"
heads = [3, 2, 3, 4, 4]
deps = ["obl", "nmod", "obj", "acl", "ROOT"]
pos = ["NOUN", "NOUN", "NOUN", "VERB", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "kızına piyano dersi verdiğim hanım "
def test_tr_noun_chunks_np_recursive_no_nmod(tr_tokenizer):
text = "içine birkaç çiçek konmuş olan bir vazo"
heads = [3, 2, 3, 6, 3, 6, 6]
deps = ["obl", "det", "nsubj", "acl", "aux", "det", "ROOT"]
pos = ["ADP", "DET", "NOUN", "VERB", "AUX", "DET", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "içine birkaç çiçek konmuş olan bir vazo "
def test_tr_noun_chunks_np_recursive_long_two_acls(tr_tokenizer):
text = "içine Simge'nin bahçesinden toplanmış birkaç çiçeğin konmuş olduğu bir vazo"
heads = [6, 2, 3, 5, 5, 6, 9, 6, 9, 9]
deps = ["obl", "nmod" , "obl", "acl", "det", "nsubj", "acl", "aux", "det", "ROOT"]
pos = ["ADP", "PROPN", "NOUN", "VERB", "DET", "NOUN", "VERB", "AUX", "DET", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "içine Simge'nin bahçesinden toplanmış birkaç çiçeğin konmuş olduğu bir vazo "
def test_tr_noun_chunks_two_nouns_in_nmod(tr_tokenizer):
text = "kız ve erkek çocuklar"
heads = [3, 2, 0, 3]
deps = ["nmod", "cc", "conj", "ROOT"]
pos = ["NOUN", "CCONJ", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "kız ve erkek çocuklar "
def test_tr_noun_chunks_two_nouns_in_nmod(tr_tokenizer):
text = "tatlı ve gürbüz çocuklar"
heads = [3, 2, 0, 3]
deps = ["amod", "cc", "conj", "ROOT"]
pos = ["ADJ", "CCONJ", "NOUN", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "tatlı ve gürbüz çocuklar "
def test_tr_noun_chunks_conj_simple(tr_tokenizer):
text = "Sen ya da ben"
heads = [0, 3, 1, 0]
deps = ["ROOT", "cc", "fixed", "conj"]
pos = ["PRON", "CCONJ", "CCONJ", "PRON"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 2
assert chunks[0].text_with_ws == "ben "
assert chunks[1].text_with_ws == "Sen "
def test_tr_noun_chunks_conj_three(tr_tokenizer):
text = "sen, ben ve ondan"
heads = [0, 2, 0, 4, 0]
deps = ["ROOT", "punct", "conj", "cc", "conj"]
pos = ["PRON", "PUNCT", "PRON", "CCONJ", "PRON"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 3
assert chunks[0].text_with_ws == "ondan "
assert chunks[1].text_with_ws == "ben "
assert chunks[2].text_with_ws == "sen "
def test_tr_noun_chunks_conj_three(tr_tokenizer):
text = "ben ya da sen ya da onlar"
heads = [0, 3, 1, 0, 6, 4, 3]
deps = ["ROOT", "cc", "fixed", "conj", "cc", "fixed", "conj"]
pos = ["PRON", "CCONJ", "CCONJ", "PRON", "CCONJ", "CCONJ", "PRON"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 3
assert chunks[0].text_with_ws == "onlar "
assert chunks[1].text_with_ws == "sen "
assert chunks[2].text_with_ws == "ben "
def test_tr_noun_chunks_conj_and_adj_phrase(tr_tokenizer):
text = "ben ve akıllı çocuk"
heads = [0, 3, 3, 0]
deps = ["ROOT", "cc", "amod", "conj"]
pos = ["PRON", "CCONJ", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 2
assert chunks[0].text_with_ws == "akıllı çocuk "
assert chunks[1].text_with_ws == "ben "
def test_tr_noun_chunks_conj_fixed_adj_phrase(tr_tokenizer):
text = "ben ya da akıllı çocuk"
heads = [0, 4, 1, 4, 0]
deps = ["ROOT", "cc", "fixed", "amod", "conj"]
pos = ["PRON", "CCONJ", "CCONJ", "ADJ", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 2
assert chunks[0].text_with_ws == "akıllı çocuk "
assert chunks[1].text_with_ws == "ben "
def test_tr_noun_chunks_conj_subject(tr_tokenizer):
text = "Sen ve ben iyi anlaşıyoruz"
heads = [4, 2, 0, 2, 4]
deps = ["nsubj", "cc", "conj", "adv", "ROOT"]
pos = ["PRON", "CCONJ", "PRON", "ADV", "VERB"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 2
assert chunks[0].text_with_ws == "ben "
assert chunks[1].text_with_ws == "Sen "
def test_tr_noun_chunks_conj_noun_head_verb(tr_tokenizer):
text = "Simge babasını görmüyormuş, annesini değil"
heads = [2, 2, 2, 4, 2, 4]
deps = ["nsubj", "obj", "ROOT", "punct", "conj", "aux"]
pos = ["PROPN", "NOUN", "VERB", "PUNCT", "NOUN", "AUX"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 3
assert chunks[0].text_with_ws == "annesini "
assert chunks[1].text_with_ws == "babasını "
assert chunks[2].text_with_ws == "Simge "
def test_tr_noun_chunks_flat_simple(tr_tokenizer):
text = "New York"
heads = [0, 0]
deps = ["ROOT", "flat"]
pos = ["PROPN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "New York "
def test_tr_noun_chunks_flat_names_and_title(tr_tokenizer):
text = "Gazi Mustafa Kemal"
heads = [1, 1, 1]
deps = ["nmod", "ROOT", "flat"]
pos = ["PROPN", "PROPN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Gazi Mustafa Kemal "
def test_tr_noun_chunks_flat_names_and_title(tr_tokenizer):
text = "Ahmet Vefik Paşa"
heads = [2, 0, 2]
deps = ["nmod", "flat", "ROOT"]
pos = ["PROPN", "PROPN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Ahmet Vefik Paşa "
def test_tr_noun_chunks_flat_name_lastname_and_title(tr_tokenizer):
text = "Cumhurbaşkanı Ahmet Necdet Sezer"
heads = [1, 1, 1, 1]
deps = ["nmod", "ROOT", "flat", "flat"]
pos = ["NOUN", "PROPN", "PROPN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Cumhurbaşkanı Ahmet Necdet Sezer "
def test_tr_noun_chunks_flat_in_nmod(tr_tokenizer):
text = "Ahmet Sezer adında bir ögrenci"
heads = [2, 0, 4, 4, 4]
deps = ["nmod", "flat", "nmod", "det", "ROOT"]
pos = ["PROPN", "PROPN", "NOUN", "DET", "NOUN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Ahmet Sezer adında bir ögrenci "
def test_tr_noun_chunks_flat_and_chain_nmod(tr_tokenizer):
text = "Batı Afrika ülkelerinden Sierra Leone"
heads = [1, 2, 3, 3, 3]
deps = ["nmod", "nmod", "nmod", "ROOT", "flat"]
pos = ["NOUN", "PROPN", "NOUN", "PROPN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text_with_ws == "Batı Afrika ülkelerinden Sierra Leone "
def test_tr_noun_chunks_two_flats_conjed(tr_tokenizer):
text = "New York ve Sierra Leone"
heads = [0, 0, 3, 0, 3]
deps = ["ROOT", "flat", "cc", "conj", "flat"]
pos = ["PROPN", "PROPN", "CCONJ", "PROPN", "PROPN"]
tokens = tr_tokenizer(text)
doc = Doc(
tokens.vocab, words=[t.text for t in tokens], pos=pos, heads=heads, deps=deps
)
chunks = list(doc.noun_chunks)
assert len(chunks) == 2
assert chunks[0].text_with_ws == "Sierra Leone "
assert chunks[1].text_with_ws == "New York "

29
spacy/tests/lang/tr/test_text.py Normal file
View File

@ -0,0 +1,29 @@
import pytest
from spacy.lang.tr.lex_attrs import like_num
@pytest.mark.parametrize(
"word",
[
"bir",
"iki",
"dört",
"altı",
"milyon",
"100",
"birinci",
"üçüncü",
"beşinci",
"100üncü",
"8inci"
]
)
def test_tr_lex_attrs_like_number_cardinal_ordinal(word):
assert like_num(word)
@pytest.mark.parametrize("word", ["beş", "yedi", "yedinci", "birinci"])
def test_tr_lex_attrs_capitals(word):
assert like_num(word)
assert like_num(word.upper())

15
spacy/tests/regression/test_issue6207.py Normal file
View File

@ -0,0 +1,15 @@
from spacy.util import filter_spans
def test_issue6207(en_tokenizer):
doc = en_tokenizer("zero one two three four five six")
# Make spans
s1 = doc[:4]
s2 = doc[3:6] # overlaps with s1
s3 = doc[5:7] # overlaps with s2, not s1
result = filter_spans((s1, s2, s3))
assert s1 in result
assert s2 not in result
assert s3 in result

2
spacy/util.py
View File

@ -1018,7 +1018,7 @@ def filter_spans(spans: Iterable["Span"]) -> List["Span"]:
# Check for end - 1 here because boundaries are inclusive
if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
result.append(span)
seen_tokens.update(range(span.start, span.end))
seen_tokens.update(range(span.start, span.end))
result = sorted(result, key=lambda span: span.start)
return result

1
website/docs/usage/rule-based-matching.md
View File

@ -167,6 +167,7 @@ rule-based matching are:
|  `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT` | Token text consists of alphabetic characters, ASCII characters, digits. ~~bool~~ |
|  `IS_LOWER`, `IS_UPPER`, `IS_TITLE` | Token text is in lowercase, uppercase, titlecase. ~~bool~~ |
|  `IS_PUNCT`, `IS_SPACE`, `IS_STOP` | Token is punctuation, whitespace, stop word. ~~bool~~ |
|  `IS_SENT_START` | Token is start of sentence. ~~bool~~ |
|  `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL` | Token text resembles a number, URL, email. ~~bool~~ |
|  `POS`, `TAG`, `MORPH`, `DEP`, `LEMMA`, `SHAPE` | The token's simple and extended part-of-speech tag, morphological analysis, dependency label, lemma, shape. ~~str~~ |
| `ENT_TYPE` | The token's entity label. ~~str~~ |

36
website/meta/universe.json
View File

@ -2542,6 +2542,42 @@
"author_links": {
"github": "abchapman93"
}
},
{
"id": "rita-dsl",
"title": "RITA DSL",
"slogan": "Domain Specific Language for creating language rules",
"github": "zaibacu/rita-dsl",
"description": "A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format",
"pip": "rita-dsl",
"thumb": "https://raw.githubusercontent.com/zaibacu/rita-dsl/master/docs/assets/logo-100px.png",
"code_language": "python",
"code_example": [
"import spacy",
"from rita.shortcuts import setup_spacy",
"",
"rules = \"\"\"",
"cuts = {\"fitted\", \"wide-cut\"}",
"lengths = {\"short\", \"long\", \"calf-length\", \"knee-length\"}",
"fabric_types = {\"soft\", \"airy\", \"crinkled\"}",
"fabrics = {\"velour\", \"chiffon\", \"knit\", \"woven\", \"stretch\"}",
"",
"{IN_LIST(cuts)?, IN_LIST(lengths), WORD(\"dress\")}->MARK(\"DRESS_TYPE\")",
"{IN_LIST(lengths), IN_LIST(cuts), WORD(\"dress\")}->MARK(\"DRESS_TYPE\")",
"{IN_LIST(fabric_types)?, IN_LIST(fabrics)}->MARK(\"DRESS_FABRIC\")",
"\"\"\"",
"",
"nlp = spacy.load(\"en\")",
"setup_spacy(nlp, rules_string=rules)",
"r = nlp(\"She was wearing a short wide-cut dress\")",
"print(list([{\"label\": e.label_, \"text\": e.text} for e in r.ents]))"
],
"category": ["standalone"],
"tags": ["dsl", "language-patterns", "language-rules", "nlp"],
"author": "Šarūnas Navickas",
"author_links": {
"github": "zaibacu"
}
}
],