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.github/contributors/mmaybeno.md
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.github/contributors/mmaybeno.md
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|
|
@ -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
|
||||
[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 | Matt Maybeno |
|
||||
| Company name (if applicable) | |
|
||||
| Title or role (if applicable) | |
|
||||
| Date | 2019-11-19 |
|
||||
| GitHub username | mmaybeno |
|
||||
| Website (optional) | |
|
||||
|
|
@ -73,7 +73,7 @@ cuda100 =
|
|||
cupy-cuda100>=5.0.0b4
|
||||
# Language tokenizers with external dependencies
|
||||
ja =
|
||||
mecab-python3==0.7
|
||||
fugashi>=0.1.3
|
||||
ko =
|
||||
natto-py==0.9.0
|
||||
th =
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
from __future__ import unicode_literals
|
||||
|
||||
from ...symbols import POS, PUNCT, SYM, ADJ, CCONJ, SCONJ, NUM, DET, ADV, ADP, X, VERB
|
||||
from ...symbols import NOUN, PROPN, PART, INTJ, PRON
|
||||
from ...symbols import NOUN, PROPN, PART, INTJ, PRON, AUX
|
||||
|
||||
|
||||
TAG_MAP = {
|
||||
|
|
@ -4249,4 +4249,20 @@ TAG_MAP = {
|
|||
"Voice": "Act",
|
||||
"Case": "Nom|Gen|Dat|Acc|Voc",
|
||||
},
|
||||
'ADJ': {POS: ADJ},
|
||||
'ADP': {POS: ADP},
|
||||
'ADV': {POS: ADV},
|
||||
'AtDf': {POS: DET},
|
||||
'AUX': {POS: AUX},
|
||||
'CCONJ': {POS: CCONJ},
|
||||
'DET': {POS: DET},
|
||||
'NOUN': {POS: NOUN},
|
||||
'NUM': {POS: NUM},
|
||||
'PART': {POS: PART},
|
||||
'PRON': {POS: PRON},
|
||||
'PROPN': {POS: PROPN},
|
||||
'SCONJ': {POS: SCONJ},
|
||||
'SYM': {POS: SYM},
|
||||
'VERB': {POS: VERB},
|
||||
'X': {POS: X},
|
||||
}
|
||||
|
|
|
|||
|
|
@ -305,6 +305,9 @@ TAG_MAP = {
|
|||
"VERB__VerbForm=Ger": {"morph": "VerbForm=Ger", POS: VERB},
|
||||
"VERB__VerbForm=Inf": {"morph": "VerbForm=Inf", POS: VERB},
|
||||
"X___": {"morph": "_", POS: X},
|
||||
"___PunctType=Quot": {POS: PUNCT},
|
||||
"___VerbForm=Inf": {POS: VERB},
|
||||
"___Number=Sing|Person=2|PronType=Prs": {POS: PRON},
|
||||
"_SP": {"morph": "_", POS: SPACE},
|
||||
}
|
||||
# fmt: on
|
||||
|
|
|
|||
|
|
@ -12,21 +12,23 @@ from ...tokens import Doc
|
|||
from ...compat import copy_reg
|
||||
from ...util import DummyTokenizer
|
||||
|
||||
# Handling for multiple spaces in a row is somewhat awkward, this simplifies
|
||||
# the flow by creating a dummy with the same interface.
|
||||
DummyNode = namedtuple("DummyNode", ["surface", "pos", "feature"])
|
||||
DummyNodeFeatures = namedtuple("DummyNodeFeatures", ["lemma"])
|
||||
DummySpace = DummyNode(' ', ' ', DummyNodeFeatures(' '))
|
||||
|
||||
ShortUnitWord = namedtuple("ShortUnitWord", ["surface", "lemma", "pos"])
|
||||
|
||||
|
||||
def try_mecab_import():
|
||||
"""Mecab is required for Japanese support, so check for it.
|
||||
def try_fugashi_import():
|
||||
"""Fugashi is required for Japanese support, so check for it.
|
||||
It it's not available blow up and explain how to fix it."""
|
||||
try:
|
||||
import MeCab
|
||||
import fugashi
|
||||
|
||||
return MeCab
|
||||
return fugashi
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Japanese support requires MeCab: "
|
||||
"https://github.com/SamuraiT/mecab-python3"
|
||||
"Japanese support requires Fugashi: "
|
||||
"https://github.com/polm/fugashi"
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -39,7 +41,7 @@ def resolve_pos(token):
|
|||
"""
|
||||
|
||||
# this is only used for consecutive ascii spaces
|
||||
if token.pos == "空白":
|
||||
if token.surface == " ":
|
||||
return "空白"
|
||||
|
||||
# TODO: This is a first take. The rules here are crude approximations.
|
||||
|
|
@ -53,55 +55,45 @@ def resolve_pos(token):
|
|||
return token.pos + ",ADJ"
|
||||
return token.pos
|
||||
|
||||
def get_words_and_spaces(tokenizer, text):
|
||||
"""Get the individual tokens that make up the sentence and handle white space.
|
||||
|
||||
Japanese doesn't usually use white space, and MeCab's handling of it for
|
||||
multiple spaces in a row is somewhat awkward.
|
||||
"""
|
||||
|
||||
tokens = tokenizer.parseToNodeList(text)
|
||||
|
||||
def detailed_tokens(tokenizer, text):
|
||||
"""Format Mecab output into a nice data structure, based on Janome."""
|
||||
node = tokenizer.parseToNode(text)
|
||||
node = node.next # first node is beginning of sentence and empty, skip it
|
||||
words = []
|
||||
spaces = []
|
||||
while node.posid != 0:
|
||||
surface = node.surface
|
||||
base = surface # a default value. Updated if available later.
|
||||
parts = node.feature.split(",")
|
||||
pos = ",".join(parts[0:4])
|
||||
if len(parts) > 7:
|
||||
# this information is only available for words in the tokenizer
|
||||
# dictionary
|
||||
base = parts[7]
|
||||
words.append(ShortUnitWord(surface, base, pos))
|
||||
|
||||
# The way MeCab stores spaces is that the rlength of the next token is
|
||||
# the length of that token plus any preceding whitespace, **in bytes**.
|
||||
# also note that this is only for half-width / ascii spaces. Full width
|
||||
# spaces just become tokens.
|
||||
scount = node.next.rlength - node.next.length
|
||||
spaces.append(bool(scount))
|
||||
while scount > 1:
|
||||
words.append(ShortUnitWord(" ", " ", "空白"))
|
||||
for token in tokens:
|
||||
# If there's more than one space, spaces after the first become tokens
|
||||
for ii in range(len(token.white_space) - 1):
|
||||
words.append(DummySpace)
|
||||
spaces.append(False)
|
||||
scount -= 1
|
||||
|
||||
node = node.next
|
||||
words.append(token)
|
||||
spaces.append(bool(token.white_space))
|
||||
return words, spaces
|
||||
|
||||
|
||||
class JapaneseTokenizer(DummyTokenizer):
|
||||
def __init__(self, cls, nlp=None):
|
||||
self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
|
||||
self.tokenizer = try_mecab_import().Tagger()
|
||||
self.tokenizer.parseToNode("") # see #2901
|
||||
self.tokenizer = try_fugashi_import().Tagger()
|
||||
self.tokenizer.parseToNodeList("") # see #2901
|
||||
|
||||
def __call__(self, text):
|
||||
dtokens, spaces = detailed_tokens(self.tokenizer, text)
|
||||
dtokens, spaces = get_words_and_spaces(self.tokenizer, text)
|
||||
words = [x.surface for x in dtokens]
|
||||
doc = Doc(self.vocab, words=words, spaces=spaces)
|
||||
mecab_tags = []
|
||||
unidic_tags = []
|
||||
for token, dtoken in zip(doc, dtokens):
|
||||
mecab_tags.append(dtoken.pos)
|
||||
unidic_tags.append(dtoken.pos)
|
||||
token.tag_ = resolve_pos(dtoken)
|
||||
token.lemma_ = dtoken.lemma
|
||||
doc.user_data["mecab_tags"] = mecab_tags
|
||||
|
||||
# if there's no lemma info (it's an unk) just use the surface
|
||||
token.lemma_ = dtoken.feature.lemma or dtoken.surface
|
||||
doc.user_data["unidic_tags"] = unidic_tags
|
||||
return doc
|
||||
|
||||
|
||||
|
|
@ -131,5 +123,4 @@ def pickle_japanese(instance):
|
|||
|
||||
copy_reg.pickle(Japanese, pickle_japanese)
|
||||
|
||||
|
||||
__all__ = ["Japanese"]
|
||||
|
|
|
|||
|
|
@ -5039,5 +5039,19 @@ TAG_MAP = {
|
|||
"punc": {POS: PUNCT},
|
||||
"v-pcp|M|P": {POS: VERB},
|
||||
"v-pcp|M|S": {POS: VERB},
|
||||
"ADJ": {POS: ADJ},
|
||||
"AUX": {POS: AUX},
|
||||
"CCONJ": {POS: CCONJ},
|
||||
"DET": {POS: DET},
|
||||
"INTJ": {POS: INTJ},
|
||||
"NUM": {POS: NUM},
|
||||
"PART": {POS: PART},
|
||||
"PRON": {POS: PRON},
|
||||
"PUNCT": {POS: PUNCT},
|
||||
"SCONJ": {POS: SCONJ},
|
||||
"SYM": {POS: SYM},
|
||||
"VERB": {POS: VERB},
|
||||
"X": {POS: X},
|
||||
"adv": {POS: ADV},
|
||||
"_SP": {POS: SPACE},
|
||||
}
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ def it_tokenizer():
|
|||
|
||||
@pytest.fixture(scope="session")
|
||||
def ja_tokenizer():
|
||||
pytest.importorskip("MeCab")
|
||||
pytest.importorskip("fugashi")
|
||||
return get_lang_class("ja").Defaults.create_tokenizer()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@
|
|||
from __future__ import unicode_literals
|
||||
from libc.string cimport memcpy
|
||||
|
||||
import numpy
|
||||
import srsly
|
||||
from collections import OrderedDict
|
||||
from thinc.neural.util import get_array_module
|
||||
|
|
@ -361,7 +360,8 @@ cdef class Vocab:
|
|||
minn = len(word)
|
||||
if maxn is None:
|
||||
maxn = len(word)
|
||||
vectors = numpy.zeros((self.vectors_length,), dtype="f")
|
||||
xp = get_array_module(self.vectors.data)
|
||||
vectors = xp.zeros((self.vectors_length,), dtype="f")
|
||||
# Fasttext's ngram computation taken from
|
||||
# https://github.com/facebookresearch/fastText
|
||||
ngrams_size = 0;
|
||||
|
|
@ -381,7 +381,7 @@ cdef class Vocab:
|
|||
j = j + 1
|
||||
if (n >= minn and not (n == 1 and (i == 0 or j == len(word)))):
|
||||
if self.strings[ngram] in self.vectors.key2row:
|
||||
vectors = numpy.add(self.vectors[self.strings[ngram]],vectors)
|
||||
vectors = xp.add(self.vectors[self.strings[ngram]], vectors)
|
||||
ngrams_size += 1
|
||||
n = n + 1
|
||||
if ngrams_size > 0:
|
||||
|
|
|
|||
|
|
@ -123,7 +123,7 @@ The L2 norm of the lexeme's vector representation.
|
|||
## Attributes {#attributes}
|
||||
|
||||
| Name | Type | Description |
|
||||
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------ |
|
||||
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `vocab` | `Vocab` | The lexeme's vocabulary. |
|
||||
| `text` | unicode | Verbatim text content. |
|
||||
| `orth` | int | ID of the verbatim text content. |
|
||||
|
|
@ -134,8 +134,8 @@ The L2 norm of the lexeme's vector representation.
|
|||
| `norm_` | unicode | The lexemes's norm, i.e. a normalized form of the lexeme text. |
|
||||
| `lower` | int | Lowercase form of the word. |
|
||||
| `lower_` | unicode | Lowercase form of the word. |
|
||||
| `shape` | int | Transform of the word's string, to show orthographic features. |
|
||||
| `shape_` | unicode | Transform of the word's string, to show orthographic features. |
|
||||
| `shape` | int | Transform of the words's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
|
||||
| `shape_` | unicode | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
|
||||
| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
|
||||
| `prefix_` | unicode | Length-N substring from the start of the word. Defaults to `N=1`. |
|
||||
| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
|
||||
|
|
|
|||
|
|
@ -409,7 +409,7 @@ The L2 norm of the token's vector representation.
|
|||
## Attributes {#attributes}
|
||||
|
||||
| Name | Type | Description |
|
||||
| -------------------------------------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| -------------------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `doc` | `Doc` | The parent document. |
|
||||
| `sent` <Tag variant="new">2.0.12</Tag> | `Span` | The sentence span that this token is a part of. |
|
||||
| `text` | unicode | Verbatim text content. |
|
||||
|
|
@ -437,8 +437,8 @@ The L2 norm of the token's vector representation.
|
|||
| `norm_` | unicode | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
|
||||
| `lower` | int | Lowercase form of the token. |
|
||||
| `lower_` | unicode | Lowercase form of the token text. Equivalent to `Token.text.lower()`. |
|
||||
| `shape` | int | Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd". |
|
||||
| `shape_` | unicode | Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd". |
|
||||
| `shape` | int | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
|
||||
| `shape_` | unicode | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
|
||||
| `prefix` | int | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. |
|
||||
| `prefix_` | unicode | A length-N substring from the start of the token. Defaults to `N=1`. |
|
||||
| `suffix` | int | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. |
|
||||
|
|
|
|||
|
|
@ -638,7 +638,7 @@ punctuation – depending on the
|
|||
|
||||
The `IS_DIGIT` flag is not very helpful here, because it doesn't tell us
|
||||
anything about the length. However, you can use the `SHAPE` flag, with each `d`
|
||||
representing a digit:
|
||||
representing a digit (up to 4 digits / characters):
|
||||
|
||||
```python
|
||||
[{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "dddd"},
|
||||
|
|
@ -654,7 +654,7 @@ match the most common formats of
|
|||
|
||||
```python
|
||||
[{"ORTH": "+"}, {"ORTH": "49"}, {"ORTH": "(", "OP": "?"}, {"SHAPE": "dddd"},
|
||||
{"ORTH": ")", "OP": "?"}, {"SHAPE": "dddddd"}]
|
||||
{"ORTH": ")", "OP": "?"}, {"SHAPE": "dddd", "LENGTH": 6}]
|
||||
```
|
||||
|
||||
Depending on the formats your application needs to match, creating an extensive
|
||||
|
|
|
|||
|
|
@ -155,7 +155,8 @@
|
|||
"name": "Japanese",
|
||||
"dependencies": [
|
||||
{ "name": "Unidic", "url": "http://unidic.ninjal.ac.jp/back_number#unidic_cwj" },
|
||||
{ "name": "Mecab", "url": "https://github.com/taku910/mecab" }
|
||||
{ "name": "Mecab", "url": "https://github.com/taku910/mecab" },
|
||||
{ "name": "fugashi", "url": "https://github.com/polm/fugashi" }
|
||||
],
|
||||
"example": "これは文章です。",
|
||||
"has_examples": true
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user