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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:
|
||||
|
|
|
|||
|
|
@ -122,44 +122,44 @@ 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. |
|
||||
| `orth_` | unicode | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
|
||||
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
|
||||
| `flags` | int | Container of the lexeme's binary flags. |
|
||||
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
|
||||
| `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. |
|
||||
| `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`. |
|
||||
| `suffix_` | unicode | Length-N substring from the start of the word. Defaults to `N=3`. |
|
||||
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
|
||||
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
|
||||
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
|
||||
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
|
||||
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
|
||||
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
|
||||
| `is_punct` | bool | Is the lexeme punctuation? |
|
||||
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
|
||||
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
|
||||
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
|
||||
| `is_bracket` | bool | Is the lexeme a bracket? |
|
||||
| `is_quote` | bool | Is the lexeme a quotation mark? |
|
||||
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
|
||||
| `like_url` | bool | Does the lexeme resemble a URL? |
|
||||
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
|
||||
| `like_email` | bool | Does the lexeme resemble an email address? |
|
||||
| `is_oov` | bool | Is the lexeme out-of-vocabulary? |
|
||||
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
|
||||
| `lang` | int | Language of the parent vocabulary. |
|
||||
| `lang_` | unicode | Language of the parent vocabulary. |
|
||||
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
|
||||
| `cluster` | int | Brown cluster ID. |
|
||||
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |
|
||||
| Name | Type | Description |
|
||||
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `vocab` | `Vocab` | The lexeme's vocabulary. |
|
||||
| `text` | unicode | Verbatim text content. |
|
||||
| `orth` | int | ID of the verbatim text content. |
|
||||
| `orth_` | unicode | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
|
||||
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
|
||||
| `flags` | int | Container of the lexeme's binary flags. |
|
||||
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
|
||||
| `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 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`. |
|
||||
| `suffix_` | unicode | Length-N substring from the start of the word. Defaults to `N=3`. |
|
||||
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
|
||||
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
|
||||
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
|
||||
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
|
||||
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
|
||||
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
|
||||
| `is_punct` | bool | Is the lexeme punctuation? |
|
||||
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
|
||||
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
|
||||
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
|
||||
| `is_bracket` | bool | Is the lexeme a bracket? |
|
||||
| `is_quote` | bool | Is the lexeme a quotation mark? |
|
||||
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
|
||||
| `like_url` | bool | Does the lexeme resemble a URL? |
|
||||
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
|
||||
| `like_email` | bool | Does the lexeme resemble an email address? |
|
||||
| `is_oov` | bool | Is the lexeme out-of-vocabulary? |
|
||||
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
|
||||
| `lang` | int | Language of the parent vocabulary. |
|
||||
| `lang_` | unicode | Language of the parent vocabulary. |
|
||||
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
|
||||
| `cluster` | int | Brown cluster ID. |
|
||||
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |
|
||||
|
|
|
|||
|
|
@ -408,71 +408,71 @@ 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. |
|
||||
| `text_with_ws` | unicode | Text content, with trailing space character if present. |
|
||||
| `whitespace_` | unicode | Trailing space character if present. |
|
||||
| `orth` | int | ID of the verbatim text content. |
|
||||
| `orth_` | unicode | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
|
||||
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
|
||||
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
|
||||
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
|
||||
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
|
||||
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
|
||||
| `i` | int | The index of the token within the parent document. |
|
||||
| `ent_type` | int | Named entity type. |
|
||||
| `ent_type_` | unicode | Named entity type. |
|
||||
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
|
||||
| `ent_iob_` | unicode | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
|
||||
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
|
||||
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | unicode | Knowledge base ID that refers to the named entity this token is a part of, if any. |
|
||||
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
|
||||
| `ent_id_` | unicode | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
|
||||
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
|
||||
| `lemma_` | unicode | Base form of the token, with no inflectional suffixes. |
|
||||
| `norm` | int | 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). |
|
||||
| `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". |
|
||||
| `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`. |
|
||||
| `suffix_` | unicode | Length-N substring from the end of the token. Defaults to `N=3`. |
|
||||
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
|
||||
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
|
||||
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
|
||||
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
|
||||
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
|
||||
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
|
||||
| `is_punct` | bool | Is the token punctuation? |
|
||||
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `(`? |
|
||||
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `)`? |
|
||||
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
|
||||
| `is_bracket` | bool | Is the token a bracket? |
|
||||
| `is_quote` | bool | Is the token a quotation mark? |
|
||||
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
|
||||
| `like_url` | bool | Does the token resemble a URL? |
|
||||
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
|
||||
| `like_email` | bool | Does the token resemble an email address? |
|
||||
| `is_oov` | bool | Is the token out-of-vocabulary? |
|
||||
| `is_stop` | bool | Is the token part of a "stop list"? |
|
||||
| `pos` | int | Coarse-grained part-of-speech. |
|
||||
| `pos_` | unicode | Coarse-grained part-of-speech. |
|
||||
| `tag` | int | Fine-grained part-of-speech. |
|
||||
| `tag_` | unicode | Fine-grained part-of-speech. |
|
||||
| `dep` | int | Syntactic dependency relation. |
|
||||
| `dep_` | unicode | Syntactic dependency relation. |
|
||||
| `lang` | int | Language of the parent document's vocabulary. |
|
||||
| `lang_` | unicode | Language of the parent document's vocabulary. |
|
||||
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
|
||||
| `idx` | int | The character offset of the token within the parent document. |
|
||||
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
|
||||
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
|
||||
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
|
||||
| `cluster` | int | Brown cluster ID. |
|
||||
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-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. |
|
||||
| `text_with_ws` | unicode | Text content, with trailing space character if present. |
|
||||
| `whitespace_` | unicode | Trailing space character if present. |
|
||||
| `orth` | int | ID of the verbatim text content. |
|
||||
| `orth_` | unicode | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
|
||||
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
|
||||
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
|
||||
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
|
||||
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
|
||||
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
|
||||
| `i` | int | The index of the token within the parent document. |
|
||||
| `ent_type` | int | Named entity type. |
|
||||
| `ent_type_` | unicode | Named entity type. |
|
||||
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
|
||||
| `ent_iob_` | unicode | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
|
||||
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
|
||||
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | unicode | Knowledge base ID that refers to the named entity this token is a part of, if any. |
|
||||
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
|
||||
| `ent_id_` | unicode | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
|
||||
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
|
||||
| `lemma_` | unicode | Base form of the token, with no inflectional suffixes. |
|
||||
| `norm` | int | 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). |
|
||||
| `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. 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`. |
|
||||
| `suffix_` | unicode | Length-N substring from the end of the token. Defaults to `N=3`. |
|
||||
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
|
||||
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
|
||||
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
|
||||
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
|
||||
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
|
||||
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
|
||||
| `is_punct` | bool | Is the token punctuation? |
|
||||
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `(`? |
|
||||
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `)`? |
|
||||
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
|
||||
| `is_bracket` | bool | Is the token a bracket? |
|
||||
| `is_quote` | bool | Is the token a quotation mark? |
|
||||
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
|
||||
| `like_url` | bool | Does the token resemble a URL? |
|
||||
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
|
||||
| `like_email` | bool | Does the token resemble an email address? |
|
||||
| `is_oov` | bool | Is the token out-of-vocabulary? |
|
||||
| `is_stop` | bool | Is the token part of a "stop list"? |
|
||||
| `pos` | int | Coarse-grained part-of-speech. |
|
||||
| `pos_` | unicode | Coarse-grained part-of-speech. |
|
||||
| `tag` | int | Fine-grained part-of-speech. |
|
||||
| `tag_` | unicode | Fine-grained part-of-speech. |
|
||||
| `dep` | int | Syntactic dependency relation. |
|
||||
| `dep_` | unicode | Syntactic dependency relation. |
|
||||
| `lang` | int | Language of the parent document's vocabulary. |
|
||||
| `lang_` | unicode | Language of the parent document's vocabulary. |
|
||||
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
|
||||
| `idx` | int | The character offset of the token within the parent document. |
|
||||
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
|
||||
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
|
||||
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
|
||||
| `cluster` | int | Brown cluster ID. |
|
||||
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
|
||||
|
|
|
|||
|
|
@ -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