mirror of https://github.com/explosion/spaCy.git
Update conllu2json MISC column handling (#4715)
Update converter to handle various things in MISC column: * `SpaceAfter=No` and set raw text accordingly * plain NER tag * name=NER (for NorNE)
This commit is contained in:
adrianeboyd
Matthew Honnibal
parent
9aab0a55e1
commit
9efd3ccbef
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@ -18,21 +18,28 @@ def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None, **_):
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"""
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# by @dvsrepo, via #11 explosion/spacy-dev-resources
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# by @katarkor
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# name=NER is to handle NorNE
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MISC_NER_PATTERN = "\|?(?:name=)?(([A-Z_]+)-([A-Z_]+)|O)\|?"
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docs = []
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raw = ""
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sentences = []
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conll_data = read_conllx(input_data, use_morphology=use_morphology)
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checked_for_ner = False
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has_ner_tags = False
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for i, example in enumerate(conll_data):
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if not checked_for_ner:
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has_ner_tags = is_ner(example.token_annotation.entities[0])
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has_ner_tags = is_ner(example.token_annotation.entities[0],
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MISC_NER_PATTERN)
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checked_for_ner = True
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sentences.append(generate_sentence(example.token_annotation, has_ner_tags))
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raw += example.text
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sentences.append(generate_sentence(example.token_annotation,
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has_ner_tags, MISC_NER_PATTERN))
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# Real-sized documents could be extracted using the comments on the
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# conllu document
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if len(sentences) % n_sents == 0:
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doc = create_doc(sentences, i)
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doc = create_doc(raw, sentences, i)
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docs.append(doc)
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raw = ""
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sentences = []
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if sentences:
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doc = create_doc(sentences, i)
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@ -40,12 +47,12 @@ def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None, **_):
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return docs
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def is_ner(tag):
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def is_ner(tag, tag_pattern):
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"""
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Check the 10th column of the first token to determine if the file contains
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NER tags
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"""
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tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
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tag_match = re.search(tag_pattern, tag)
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if tag_match:
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return True
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elif tag == "O":
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@ -63,9 +70,10 @@ def read_conllx(input_data, use_morphology=False, n=0):
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while lines[0].startswith("#"):
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lines.pop(0)
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ids, words, tags, heads, deps, ents = [], [], [], [], [], []
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spaces = []
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for line in lines:
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parts = line.split("\t")
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id_, word, lemma, pos, tag, morph, head, dep, _1, iob = parts
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id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
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if "-" in id_ or "." in id_:
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continue
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try:
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@ -74,18 +82,27 @@ def read_conllx(input_data, use_morphology=False, n=0):
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dep = "ROOT" if dep == "root" else dep
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tag = pos if tag == "_" else tag
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tag = tag + "__" + morph if use_morphology else tag
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iob = iob if iob else "O"
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ent = misc if misc else "O"
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ids.append(id_)
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words.append(word)
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tags.append(tag)
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heads.append(head)
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deps.append(dep)
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ents.append(iob)
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ents.append(ent)
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if "SpaceAfter=No" in misc:
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spaces.append(False)
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else:
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spaces.append(True)
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except: # noqa: E722
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print(line)
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raise
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example = Example(doc=None)
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raw = ""
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for word, space in zip(words, spaces):
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raw += word
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if space:
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raw += " "
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example = Example(doc=raw)
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example.set_token_annotation(ids=ids, words=words, tags=tags,
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heads=heads, deps=deps, entities=ents)
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yield example
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@ -94,7 +111,7 @@ def read_conllx(input_data, use_morphology=False, n=0):
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break
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def simplify_tags(iob):
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def simplify_tags(iob, tag_pattern):
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"""
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Simplify tags obtained from the dataset in order to follow Wikipedia
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scheme (PER, LOC, ORG, MISC). 'PER', 'LOC' and 'ORG' keep their tags, while
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@ -103,26 +120,28 @@ def simplify_tags(iob):
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"""
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new_iob = []
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for tag in iob:
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tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
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tag_match = re.search(tag_pattern, tag)
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new_tag = "O"
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if tag_match:
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prefix = tag_match.group(1)
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suffix = tag_match.group(2)
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prefix = tag_match.group(2)
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suffix = tag_match.group(3)
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if prefix and suffix:
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if suffix == "GPE_LOC":
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suffix = "LOC"
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elif suffix == "GPE_ORG":
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suffix = "ORG"
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elif suffix != "PER" and suffix != "LOC" and suffix != "ORG":
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suffix = "MISC"
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tag = prefix + "-" + suffix
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new_iob.append(tag)
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new_tag = prefix + "-" + suffix
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new_iob.append(new_tag)
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return new_iob
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def generate_sentence(token_annotation, has_ner_tags):
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def generate_sentence(token_annotation, has_ner_tags, tag_pattern):
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sentence = {}
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tokens = []
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if has_ner_tags:
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iob = simplify_tags(token_annotation.entities)
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iob = simplify_tags(token_annotation.entities, tag_pattern)
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biluo = iob_to_biluo(iob)
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for i, id in enumerate(token_annotation.ids):
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token = {}
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@ -138,11 +157,12 @@ def generate_sentence(token_annotation, has_ner_tags):
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return sentence
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def create_doc(sentences, id):
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def create_doc(raw, sentences, id):
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doc = {}
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paragraph = {}
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doc["id"] = id
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doc["paragraphs"] = []
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paragraph["raw"] = raw.strip()
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paragraph["sentences"] = sentences
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doc["paragraphs"].append(paragraph)
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return doc
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@ -32,6 +32,32 @@ def test_cli_converters_conllu2json():
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assert [t["ner"] for t in tokens] == ["O", "B-PER", "L-PER", "O"]
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def test_cli_converters_conllu2json():
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# https://raw.githubusercontent.com/ohenrik/nb_news_ud_sm/master/original_data/no-ud-dev-ner.conllu
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lines = [
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|name=B-PER",
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"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tname=I-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O",
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"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=O",
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]
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input_data = "\n".join(lines)
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converted = conllu2json(input_data, n_sents=1)
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assert len(converted) == 1
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert converted[0]["paragraphs"][0]["raw"] == "Dommer FinnEilertsen avstår."
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
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sent = converted[0]["paragraphs"][0]["sentences"][0]
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assert len(sent["tokens"]) == 5
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tokens = sent["tokens"]
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assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår", "."]
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assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB", "PUNCT"]
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assert [t["head"] for t in tokens] == [1, 2, -1, 0, -1]
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assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT", "punct"]
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assert [t["ner"] for t in tokens] == ["O", "B-PER", "L-PER", "O", "O"]
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def test_cli_converters_iob2json():
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lines = [
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"I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
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@ -106,7 +132,6 @@ def test_cli_converters_conll_ner2json():
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]
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input_data = "\n".join(lines)
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converted = conll_ner2json(input_data, n_sents=10)
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print(converted)
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assert len(converted) == 1
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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