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Ines Montani 2019-08-06 12:13:53 +02:00
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.github/contributors/jenojp.md vendored Normal file
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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 | Jeno Pizarro |
| Company name (if applicable) | IBM |
| Title or role (if applicable) | Data Scientist and Software Developer |
| Date | 3 August 2019 |
| GitHub username | jenojp |
| Website (optional) | https://www.linkedin.com/in/jenojp/ |

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spacy/errors.py
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@ -417,6 +417,10 @@ class Errors(object):
"component matches the model being loaded.")
E150 = ("The language of the `nlp` object and the `vocab` should be the same, "
"but found '{nlp}' and '{vocab}' respectively.")
E151 = ("Trying to call nlp.update without required annotation types. "
"Expected top-level keys: {expected_keys}."
" Got: {unexpected_keys}.")
@add_codes
class TempErrors(object):

4
spacy/language.py
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@ -445,6 +445,10 @@ class Language(object):
if isinstance(doc, basestring_):
doc = self.make_doc(doc)
if not isinstance(gold, GoldParse):
expected_keys = ("words", "tags", "heads", "deps", "entities", "cats", "links")
unexpected_keys = [k for k in gold if k not in expected_keys]
if unexpected_keys:
raise ValueError(Errors.E151.format(unexpected_keys=unexpected_keys, expected_keys=expected_keys))
gold = GoldParse(doc, **gold)
doc_objs.append(doc)
gold_objs.append(gold)

1
spacy/tests/regression/test_issue4002.py
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@ -6,7 +6,6 @@ from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc
@pytest.mark.xfail
def test_issue4002(en_vocab):
"""Test that the PhraseMatcher can match on overwritten NORM attributes.
"""

3
spacy/tests/test_language.py
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@ -22,6 +22,7 @@ def nlp():
def test_language_update(nlp):
text = "hello world"
annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
wrongkeyannots = {"LABEL": True}
doc = Doc(nlp.vocab, words=text.split(" "))
gold = GoldParse(doc, **annots)
# Update with doc and gold objects
@ -37,6 +38,8 @@ def test_language_update(nlp):
nlp.update([doc], [])
with pytest.raises(IndexError):
nlp.update([], [gold])
with pytest.raises(ValueError):
nlp.update([text], [wrongkeyannots])
def test_language_evaluate(nlp):

4
spacy/tokens/doc.pyx
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@ -49,6 +49,10 @@ cdef int bounds_check(int i, int length, int padding) except -1:
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
if feat_name == LEMMA:
return token.lemma
elif feat_name == NORM:
if not token.norm:
return token.lex.norm
return token.norm
elif feat_name == POS:
return token.pos
elif feat_name == TAG:

63
website/docs/usage/rule-based-matching.md
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@ -153,24 +153,54 @@ processes.
#### Available token attributes {#adding-patterns-attributes}
The available token pattern keys are uppercase versions of the
[`Token` attributes](/api/token#attributes). The most relevant ones for
The available token pattern keys correspond to a number of
[`Token` attributes](/api/token#attributes). The supported attributes for
rule-based matching are:
| Attribute | Type |  Description |
| -------------------------------------- | ------- | ------------------------------------------------------------------------------------------------ |
| `ORTH` | unicode | The exact verbatim text of a token. |
| `TEXT` <Tag variant="new">2.1</Tag> | unicode | The exact verbatim text of a token. |
| `LOWER` | unicode | The lowercase form of the token text. |
|  `LENGTH` | int | The length of the token text. |
|  `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT` | bool | Token text consists of alphanumeric characters, ASCII characters, digits. |
|  `IS_LOWER`, `IS_UPPER`, `IS_TITLE` | bool | Token text is in lowercase, uppercase, titlecase. |
|  `IS_PUNCT`, `IS_SPACE`, `IS_STOP` | bool | Token is punctuation, whitespace, stop word. |
|  `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL` | bool | Token text resembles a number, URL, email. |
|  `POS`, `TAG`, `DEP`, `LEMMA`, `SHAPE` | unicode | The token's simple and extended part-of-speech tag, dependency label, lemma, shape. |
| `ENT_TYPE` | unicode | The token's entity label. |
| Attribute | Type |  Description |
| -------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------ |
| `ORTH` | unicode | The exact verbatim text of a token. |
| `TEXT` <Tag variant="new">2.1</Tag> | unicode | The exact verbatim text of a token. |
| `LOWER` | unicode | The lowercase form of the token text. |
|  `LENGTH` | int | The length of the token text. |
|  `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT` | bool | Token text consists of alphanumeric characters, ASCII characters, digits. |
|  `IS_LOWER`, `IS_UPPER`, `IS_TITLE` | bool | Token text is in lowercase, uppercase, titlecase. |
|  `IS_PUNCT`, `IS_SPACE`, `IS_STOP` | bool | Token is punctuation, whitespace, stop word. |
|  `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL` | bool | Token text resembles a number, URL, email. |
|  `POS`, `TAG`, `DEP`, `LEMMA`, `SHAPE` | unicode | The token's simple and extended part-of-speech tag, dependency label, lemma, shape. |
| `ENT_TYPE` | unicode | The token's entity label. |
| `_` <Tag variant="new">2.1</Tag> | dict | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). |
<Accordion title="Does it matter if the attribute names are uppercase or lowercase?">
No, it shouldn't. spaCy will normalize the names internally and
`{"LOWER": "text"}` and `{"lower": "text"}` will both produce the same result.
Using the uppercase version is mostly a convention to make it clear that the
attributes are "special" and don't exactly map to the token attributes like
`Token.lower` and `Token.lower_`.
</Accordion>
<Accordion title="Why are not all token attributes supported?">
spaCy can't provide access to all of the attributes because the `Matcher` loops
over the Cython data, not the Python objects. Inside the matcher, we're dealing
with a [`TokenC` struct](/api/cython-structs#tokenc) – we don't have an instance
of [`Token`](/api/token). This means that all of the attributes that refer to
computed properties can't be accessed.
The uppercase attribute names like `LOWER` or `IS_PUNCT` refer to symbols from
the
[`spacy.attrs`](https://github.com/explosion/spaCy/tree/master/spacy/attrs.pyx)
enum table. They're passed into a function that essentially is a big case/switch
statement, to figure out which struct field to return. The same attribute
identifiers are used in [`Doc.to_array`](/api/doc#to_array), and a few other
places in the code where you need to describe fields like this.
</Accordion>
---
<Infobox title="Tip: Try the interactive matcher explorer">
[![Matcher demo](../images/matcher-demo.jpg)](https://explosion.ai/demos/matcher)
@ -1140,8 +1170,9 @@ To apply this logic automatically when we process a text, we can add it to the
above logic also expects that entities are merged into single tokens. spaCy
ships with a handy built-in `merge_entities` that takes care of that. Instead of
just printing the result, you could also write it to
[custom attributes](/usage/processing-pipelines#custom-components-attributes) on the
entity `Span` – for example `._.orgs` or `._.prev_orgs` and `._.current_orgs`.
[custom attributes](/usage/processing-pipelines#custom-components-attributes) on
the entity `Span` – for example `._.orgs` or `._.prev_orgs` and
`._.current_orgs`.
> #### Merging entities
>