import re from .conll_ner_to_docs import n_sents_info from ...training import iob_to_biluo, biluo_tags_to_spans from ...tokens import Doc, Token, Span from ...vocab import Vocab from wasabi import Printer def conllu_to_docs( 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) sent_docs = read_conllx( input_data, append_morphology=append_morphology, ner_tag_pattern=MISC_NER_PATTERN, ner_map=ner_map, merge_subtokens=merge_subtokens, ) sent_docs_to_merge = [] for sent_doc in sent_docs: sent_docs_to_merge.append(sent_doc) if len(sent_docs_to_merge) % n_sents == 0: yield Doc.from_docs(sent_docs_to_merge) sent_docs_to_merge = [] if sent_docs_to_merge: yield Doc.from_docs(sent_docs_to_merge) 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 docs, one for each sentence """ vocab = 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) doc = conllu_sentence_to_doc( vocab, lines, ner_tag_pattern, merge_subtokens=merge_subtokens, append_morphology=append_morphology, ner_map=ner_map, ) yield doc 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 conllu_sentence_to_doc( 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, tags=tags, pos=poses, deps=deps, lemmas=lemmas, morphs=morphs, heads=heads, ) for i in range(len(doc)): 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 = biluo_tags_to_spans(doc, ents) if merge_subtokens: doc = merge_conllu_subtokens(lines, doc) # create final Doc from custom Doc annotation words, spaces, tags, morphs, lemmas, poses = [], [], [], [], [], [] heads, deps = [], [] 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_) poses.append(t.pos_) heads.append(t.head.i) deps.append(t.dep_) doc_x = Doc( vocab, words=words, spaces=spaces, tags=tags, morphs=morphs, lemmas=lemmas, pos=poses, deps=deps, heads=heads, ) doc_x.ents = [Span(doc_x, ent.start, ent.end, label=ent.label) for ent in doc.ents] return doc_x 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