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fix a bug causing mis-alignments (#5560)

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Hiroshi Matsuda 2020-06-08 22:49:34 +09:00 committed by GitHub
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2 changed files with 169 additions and 19 deletions
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106
.github/contributors/hiroshi-matsuda-rit.md vendored Normal file
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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 | Hiroshi Matsuda |
| Company name (if applicable) | Megagon Labs, Tokyo |
| Title or role (if applicable) | Research Scientist |
| Date | June 6, 2020 |
| GitHub username | hiroshi-matsuda-rit |
| Website (optional) | |

82
spacy/lang/ja/__init__.py
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@ -1,7 +1,6 @@
# encoding: utf8
from __future__ import unicode_literals, print_function
import re
from collections import namedtuple
from .stop_words import STOP_WORDS
@ -14,7 +13,9 @@ from ...compat import copy_reg
from ...language import Language
from ...symbols import POS
from ...tokens import Doc
from ...util import DummyTokenizer, get_words_and_spaces
from ...util import DummyTokenizer
from ...errors import Errors
# Hold the attributes we need with convenient names
DetailedToken = namedtuple("DetailedToken", ["surface", "pos", "lemma"])
@ -41,7 +42,7 @@ def try_sudachi_import():
)
def resolve_pos(token, next_token):
def resolve_pos(orth, pos, next_pos):
"""If necessary, add a field to the POS tag for UD mapping.
Under Universal Dependencies, sometimes the same Unidic POS tag can
be mapped differently depending on the literal token or its context
@ -53,22 +54,22 @@ def resolve_pos(token, next_token):
# token.
# orth based rules
if token.pos in TAG_ORTH_MAP:
orth_map = TAG_ORTH_MAP[token.pos[0]]
if token.surface in orth_map:
return orth_map[token.surface], None
if pos[0] in TAG_ORTH_MAP:
orth_map = TAG_ORTH_MAP[pos[0]]
if orth in orth_map:
return orth_map[orth], None
# tag bi-gram mapping
if next_token:
tag_bigram = token.pos[0], next_token.pos[0]
if next_pos:
tag_bigram = pos[0], next_pos[0]
if tag_bigram in TAG_BIGRAM_MAP:
bipos = TAG_BIGRAM_MAP[tag_bigram]
if bipos[0] is None:
return TAG_MAP[token.pos[0]][POS], bipos[1]
return TAG_MAP[pos[0]][POS], bipos[1]
else:
return bipos
return TAG_MAP[token.pos[0]][POS], None
return TAG_MAP[pos[0]][POS], None
# Use a mapping of paired punctuation to avoid splitting quoted sentences.
@ -120,6 +121,48 @@ def get_dtokens(tokenizer, text):
words = [ww for ww in words if len(ww.surface) > 0]
return words
def get_words_lemmas_tags_spaces(dtokens, text, gap_tag=("空白", "")):
words = [x.surface for x in dtokens]
if "".join("".join(words).split()) != "".join(text.split()):
raise ValueError(Errors.E194.format(text=text, words=words))
text_words = []
text_lemmas = []
text_tags = []
text_spaces = []
text_pos = 0
# normalize words to remove all whitespace tokens
norm_words, norm_dtokens = zip(*[(word, dtokens) for word, dtokens in zip(words, dtokens) if not word.isspace()])
# align words with text
for word, dtoken in zip(norm_words, norm_dtokens):
try:
word_start = text[text_pos:].index(word)
except ValueError:
raise ValueError(Errors.E194.format(text=text, words=words))
if word_start > 0:
w = text[text_pos:text_pos + word_start]
text_words.append(w)
text_lemmas.append(w)
text_tags.append(gap_tag)
text_spaces.append(False)
text_pos += word_start
text_words.append(word)
text_lemmas.append(dtoken.lemma)
text_tags.append(dtoken.pos)
text_spaces.append(False)
text_pos += len(word)
if text_pos < len(text) and text[text_pos] == " ":
text_spaces[-1] = True
text_pos += 1
if text_pos < len(text):
w = text[text_pos:]
text_words.append(w)
text_lemmas.append(w)
text_tags.append(gap_tag)
text_spaces.append(False)
return text_words, text_lemmas, text_tags, text_spaces
class JapaneseTokenizer(DummyTokenizer):
def __init__(self, cls, nlp=None):
self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
@ -128,22 +171,23 @@ class JapaneseTokenizer(DummyTokenizer):
def __call__(self, text):
dtokens = get_dtokens(self.tokenizer, text)
words = [x.surface for x in dtokens]
words, spaces = get_words_and_spaces(words, text)
unidic_tags = [",".join(x.pos) for x in dtokens]
words, lemmas, unidic_tags, spaces = get_words_lemmas_tags_spaces(dtokens, text)
doc = Doc(self.vocab, words=words, spaces=spaces)
next_pos = None
for ii, (token, dtoken) in enumerate(zip(doc, dtokens)):
ntoken = dtokens[ii+1] if ii+1 < len(dtokens) else None
token.tag_ = dtoken.pos[0]
for idx, (token, lemma, unidic_tag) in enumerate(zip(doc, lemmas, unidic_tags)):
token.tag_ = unidic_tag[0]
if next_pos:
token.pos = next_pos
next_pos = None
else:
token.pos, next_pos = resolve_pos(dtoken, ntoken)
token.pos, next_pos = resolve_pos(
token.orth_,
unidic_tag,
unidic_tags[idx + 1] if idx + 1 < len(unidic_tags) else None
)
# if there's no lemma info (it's an unk) just use the surface
token.lemma_ = dtoken.lemma
token.lemma_ = lemma
doc.user_data["unidic_tags"] = unidic_tags
separate_sentences(doc)