spaCy/spacy/cli/converters/conllu2json.py

349 lines
12 KiB
Python

import re
from ...gold import Example
from ...gold import iob_to_biluo, spans_from_biluo_tags, biluo_tags_from_offsets
from ...language import Language
from ...tokens import Doc, Token
from .conll_ner2json import n_sents_info
from wasabi import Printer
def conllu2json(
input_data,
n_sents=10,
append_morphology=False,
lang=None,
ner_map=None,
merge_subtokens=False,
no_print=False,
**_
):
"""
Convert conllu files into JSON format for use with train cli.
append_morphology parameter enables appending morphology to tags, which is
useful for languages such as Spanish, where UD tags are not so rich.
Extract NER tags if available and convert them so that they follow
BILUO and the Wikipedia scheme
"""
MISC_NER_PATTERN = "\|?(?:name=)?(([A-Z_]+)-([A-Z_]+)|O)\|?"
msg = Printer(no_print=no_print)
n_sents_info(msg, n_sents)
docs = []
raw = ""
sentences = []
conll_data = read_conllx(
input_data,
append_morphology=append_morphology,
ner_tag_pattern=MISC_NER_PATTERN,
ner_map=ner_map,
merge_subtokens=merge_subtokens,
)
has_ner_tags = has_ner(input_data, ner_tag_pattern=MISC_NER_PATTERN)
for i, example in enumerate(conll_data):
raw += example.text
sentences.append(
generate_sentence(
example.token_annotation,
has_ner_tags,
MISC_NER_PATTERN,
ner_map=ner_map,
)
)
# Real-sized documents could be extracted using the comments on the
# conllu document
if len(sentences) % n_sents == 0:
doc = create_json_doc(raw, sentences, i)
docs.append(doc)
raw = ""
sentences = []
if sentences:
doc = create_json_doc(raw, sentences, i)
docs.append(doc)
return docs
def has_ner(input_data, ner_tag_pattern):
"""
Check the 10th column of the first token to determine if the file contains
NER tags
"""
for sent in input_data.strip().split("\n\n"):
lines = sent.strip().split("\n")
if lines:
while lines[0].startswith("#"):
lines.pop(0)
if lines:
parts = lines[0].split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if re.search(ner_tag_pattern, misc):
return True
else:
return False
def read_conllx(
input_data,
append_morphology=False,
merge_subtokens=False,
ner_tag_pattern="",
ner_map=None,
):
""" Yield examples, one for each sentence """
vocab = Language.Defaults.create_vocab() # need vocab to make a minimal Doc
for sent in input_data.strip().split("\n\n"):
lines = sent.strip().split("\n")
if lines:
while lines[0].startswith("#"):
lines.pop(0)
example = example_from_conllu_sentence(
vocab,
lines,
ner_tag_pattern,
merge_subtokens=merge_subtokens,
append_morphology=append_morphology,
ner_map=ner_map,
)
yield example
def get_entities(lines, tag_pattern, ner_map=None):
"""Find entities in the MISC column according to the pattern and map to
final entity type with `ner_map` if mapping present. Entity tag is 'O' if
the pattern is not matched.
lines (unicode): CONLL-U lines for one sentences
tag_pattern (unicode): Regex pattern for entity tag
ner_map (dict): Map old NER tag names to new ones, '' maps to O.
RETURNS (list): List of BILUO entity tags
"""
miscs = []
for line in lines:
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "-" in id_ or "." in id_:
continue
miscs.append(misc)
iob = []
for misc in miscs:
tag_match = re.search(tag_pattern, misc)
iob_tag = "O"
if tag_match:
prefix = tag_match.group(2)
suffix = tag_match.group(3)
if prefix and suffix:
iob_tag = prefix + "-" + suffix
if ner_map:
suffix = ner_map.get(suffix, suffix)
if suffix == "":
iob_tag = "O"
else:
iob_tag = prefix + "-" + suffix
iob.append(iob_tag)
return iob_to_biluo(iob)
def generate_sentence(token_annotation, has_ner_tags, tag_pattern, ner_map=None):
sentence = {}
tokens = []
for i, id_ in enumerate(token_annotation.ids):
token = {}
token["id"] = id_
token["orth"] = token_annotation.get_word(i)
token["tag"] = token_annotation.get_tag(i)
token["pos"] = token_annotation.get_pos(i)
token["lemma"] = token_annotation.get_lemma(i)
token["morph"] = token_annotation.get_morph(i)
token["head"] = token_annotation.get_head(i) - id_
token["dep"] = token_annotation.get_dep(i)
if has_ner_tags:
token["ner"] = token_annotation.get_entity(i)
tokens.append(token)
sentence["tokens"] = tokens
return sentence
def create_json_doc(raw, sentences, id_):
doc = {}
paragraph = {}
doc["id"] = id_
doc["paragraphs"] = []
paragraph["raw"] = raw.strip()
paragraph["sentences"] = sentences
doc["paragraphs"].append(paragraph)
return doc
def example_from_conllu_sentence(
vocab,
lines,
ner_tag_pattern,
merge_subtokens=False,
append_morphology=False,
ner_map=None,
):
"""Create an Example from the lines for one CoNLL-U sentence, merging
subtokens and appending morphology to tags if required.
lines (unicode): The non-comment lines for a CoNLL-U sentence
ner_tag_pattern (unicode): The regex pattern for matching NER in MISC col
RETURNS (Example): An example containing the annotation
"""
# create a Doc with each subtoken as its own token
# if merging subtokens, each subtoken orth is the merged subtoken form
if not Token.has_extension("merged_orth"):
Token.set_extension("merged_orth", default="")
if not Token.has_extension("merged_lemma"):
Token.set_extension("merged_lemma", default="")
if not Token.has_extension("merged_morph"):
Token.set_extension("merged_morph", default="")
if not Token.has_extension("merged_spaceafter"):
Token.set_extension("merged_spaceafter", default="")
words, spaces, tags, poses, morphs, lemmas = [], [], [], [], [], []
heads, deps = [], []
subtok_word = ""
in_subtok = False
for i in range(len(lines)):
line = lines[i]
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "." in id_:
continue
if "-" in id_:
in_subtok = True
if "-" in id_:
in_subtok = True
subtok_word = word
subtok_start, subtok_end = id_.split("-")
subtok_spaceafter = "SpaceAfter=No" not in misc
continue
if merge_subtokens and in_subtok:
words.append(subtok_word)
else:
words.append(word)
if in_subtok:
if id_ == subtok_end:
spaces.append(subtok_spaceafter)
else:
spaces.append(False)
elif "SpaceAfter=No" in misc:
spaces.append(False)
else:
spaces.append(True)
if in_subtok and id_ == subtok_end:
subtok_word = ""
in_subtok = False
id_ = int(id_) - 1
head = (int(head) - 1) if head not in ("0", "_") else id_
tag = pos if tag == "_" else tag
morph = morph if morph != "_" else ""
dep = "ROOT" if dep == "root" else dep
lemmas.append(lemma)
poses.append(pos)
tags.append(tag)
morphs.append(morph)
heads.append(head)
deps.append(dep)
doc = Doc(vocab, words=words, spaces=spaces)
for i in range(len(doc)):
doc[i].tag_ = tags[i]
doc[i].pos_ = poses[i]
doc[i].dep_ = deps[i]
doc[i].lemma_ = lemmas[i]
doc[i].head = doc[heads[i]]
doc[i]._.merged_orth = words[i]
doc[i]._.merged_morph = morphs[i]
doc[i]._.merged_lemma = lemmas[i]
doc[i]._.merged_spaceafter = spaces[i]
ents = get_entities(lines, ner_tag_pattern, ner_map)
doc.ents = spans_from_biluo_tags(doc, ents)
doc.is_parsed = True
doc.is_tagged = True
if merge_subtokens:
doc = merge_conllu_subtokens(lines, doc)
# create Example from custom Doc annotation
ids, words, tags, heads, deps = [], [], [], [], []
pos, lemmas, morphs, spaces = [], [], [], []
for i, t in enumerate(doc):
ids.append(i)
words.append(t._.merged_orth)
if append_morphology and t._.merged_morph:
tags.append(t.tag_ + "__" + t._.merged_morph)
else:
tags.append(t.tag_)
pos.append(t.pos_)
morphs.append(t._.merged_morph)
lemmas.append(t._.merged_lemma)
heads.append(t.head.i)
deps.append(t.dep_)
spaces.append(t._.merged_spaceafter)
ent_offsets = [(e.start_char, e.end_char, e.label_) for e in doc.ents]
ents = biluo_tags_from_offsets(doc, ent_offsets)
raw = ""
for word, space in zip(words, spaces):
raw += word
if space:
raw += " "
example = Example(doc=raw)
example.set_token_annotation(
ids=ids,
words=words,
tags=tags,
pos=pos,
morphs=morphs,
lemmas=lemmas,
heads=heads,
deps=deps,
entities=ents,
)
return example
def merge_conllu_subtokens(lines, doc):
# identify and process all subtoken spans to prepare attrs for merging
subtok_spans = []
for line in lines:
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "-" in id_:
subtok_start, subtok_end = id_.split("-")
subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]
subtok_spans.append(subtok_span)
# create merged tag, morph, and lemma values
tags = []
morphs = {}
lemmas = []
for token in subtok_span:
tags.append(token.tag_)
lemmas.append(token.lemma_)
if token._.merged_morph:
for feature in token._.merged_morph.split("|"):
field, values = feature.split("=", 1)
if field not in morphs:
morphs[field] = set()
for value in values.split(","):
morphs[field].add(value)
# create merged features for each morph field
for field, values in morphs.items():
morphs[field] = field + "=" + ",".join(sorted(values))
# set the same attrs on all subtok tokens so that whatever head the
# retokenizer chooses, the final attrs are available on that token
for token in subtok_span:
token._.merged_orth = token.orth_
token._.merged_lemma = " ".join(lemmas)
token.tag_ = "_".join(tags)
token._.merged_morph = "|".join(sorted(morphs.values()))
token._.merged_spaceafter = (
True if subtok_span[-1].whitespace_ else False
)
with doc.retokenize() as retokenizer:
for span in subtok_spans:
retokenizer.merge(span)
return doc