mirror of
https://github.com/explosion/spaCy.git
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00842d7f1b
* merging conllu/conll and conllubio scripts * tabs to spaces * removing conllubio2json from converters/__init__.py * Move not-really-CLI tests to misc * Add converter test using no-ud data * Fix test I broke * removing include_biluo parameter * fixing read_conllx * remove include_biluo from convert.py
139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import re
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from ...gold import iob_to_biluo
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def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None):
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"""
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Convert conllu files into JSON format for use with train cli.
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use_morphology parameter enables appending morphology to tags, which is
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useful for languages such as Spanish, where UD tags are not so rich.
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Extract NER tags if available and convert them so that they follow
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BILUO and the Wikipedia scheme
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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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docs = []
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sentences = []
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conll_tuples = 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, (raw_text, tokens) in enumerate(conll_tuples):
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sentence, brackets = tokens[0]
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if not checked_for_ner:
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has_ner_tags = is_ner(sentence[5][0])
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checked_for_ner = True
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sentences.append(generate_sentence(sentence, has_ner_tags))
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# Real-sized documents could be extracted using the comments on the
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# conluu document
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if len(sentences) % n_sents == 0:
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doc = create_doc(sentences, i)
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docs.append(doc)
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sentences = []
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return docs
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def is_ner(tag):
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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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if tag_match:
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return True
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elif tag == "O":
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return True
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else:
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return False
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def read_conllx(input_data, use_morphology=False, n=0):
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i = 0
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for sent in input_data.strip().split("\n\n"):
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lines = sent.strip().split("\n")
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if lines:
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while lines[0].startswith("#"):
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lines.pop(0)
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tokens = []
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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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if "-" in id_ or "." in id_:
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continue
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try:
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id_ = int(id_) - 1
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head = (int(head) - 1) if head != "0" else id_
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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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tokens.append((id_, word, tag, head, dep, iob))
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except: # noqa: E722
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print(line)
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raise
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tuples = [list(t) for t in zip(*tokens)]
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yield (None, [[tuples, []]])
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i += 1
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if n >= 1 and i >= n:
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break
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def simplify_tags(iob):
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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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'GPE_LOC' is simplified to 'LOC', 'GPE_ORG' to 'ORG' and all remaining tags to
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'MISC'.
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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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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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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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return new_iob
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def generate_sentence(sent, has_ner_tags):
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(id_, word, tag, head, dep, iob) = sent
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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(iob)
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biluo = iob_to_biluo(iob)
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for i, id in enumerate(id_):
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token = {}
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token["id"] = id
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token["orth"] = word[i]
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token["tag"] = tag[i]
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token["head"] = head[i] - id
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token["dep"] = dep[i]
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if has_ner_tags:
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token["ner"] = biluo[i]
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tokens.append(token)
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sentence["tokens"] = tokens
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return sentence
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def create_doc(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["sentences"] = sentences
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doc["paragraphs"].append(paragraph)
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return doc
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