spaCy/spacy/gold/converters/conllu2json.py

332 lines
11 KiB
Python

import re
from .conll_ner2docs import n_sents_info
from ...gold import Example
from ...gold import iob_to_biluo, spans_from_biluo_tags
from ...language import Language
from ...tokens import Doc, Token
from wasabi import Printer
def conllu2json(
input_data,
n_sents=10,
append_morphology=False,
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|NE)=)?([BILU])-([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, MISC_NER_PATTERN)
for i, example in enumerate(conll_data):
raw += example.text
sentences.append(
generate_sentence(
example.to_dict(), 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 MISC column for 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)
for line in lines:
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
for misc_part in misc.split("|"):
if re.match(ner_tag_pattern, misc_part):
return True
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 (str): CONLL-U lines for one sentences
tag_pattern (str): 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:
iob_tag = "O"
for misc_part in misc.split("|"):
tag_match = re.match(tag_pattern, misc_part)
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
break
iob.append(iob_tag)
return iob_to_biluo(iob)
def generate_sentence(example_dict, has_ner_tags, tag_pattern, ner_map=None):
sentence = {}
tokens = []
token_annotation = example_dict["token_annotation"]
for i, id_ in enumerate(token_annotation["ids"]):
token = {}
token["id"] = id_
token["orth"] = token_annotation["words"][i]
token["tag"] = token_annotation["tags"][i]
token["pos"] = token_annotation["pos"][i]
token["lemma"] = token_annotation["lemmas"][i]
token["morph"] = token_annotation["morphs"][i]
token["head"] = token_annotation["heads"][i] - i
token["dep"] = token_annotation["deps"][i]
if has_ner_tags:
token["ner"] = example_dict["doc_annotation"]["entities"][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 (str): The non-comment lines for a CoNLL-U sentence
ner_tag_pattern (str): 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
words, spaces, tags, morphs, lemmas = [], [], [], [], []
for i, t in enumerate(doc):
words.append(t._.merged_orth)
lemmas.append(t._.merged_lemma)
spaces.append(t._.merged_spaceafter)
morphs.append(t._.merged_morph)
if append_morphology and t._.merged_morph:
tags.append(t.tag_ + "__" + t._.merged_morph)
else:
tags.append(t.tag_)
doc_x = Doc(vocab, words=words, spaces=spaces)
ref_dict = Example(doc_x, reference=doc).to_dict()
ref_dict["words"] = words
ref_dict["lemmas"] = lemmas
ref_dict["spaces"] = spaces
ref_dict["tags"] = tags
ref_dict["morphs"] = morphs
example = Example.from_dict(doc_x, ref_dict)
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