"""(A publicly available subsample of) a reference corpus of Slovene texts.""" import glob import logging import os import os.path import re import xml.etree.ElementTree as ET from copy import deepcopy import datasets XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}" def namespace(element): # https://stackoverflow.com/a/12946675 m = re.match(r'\{.*\}', element.tag) return m.group(0) if m else '' _CITATION = """\ @misc{ccGigafida, title = {Written corpus {ccGigafida} 1.0}, author = {Logar, Nata{\v s}a and Erjavec, Toma{\v z} and Krek, Simon and Gr{\v c}ar, Miha and Holozan, Peter}, url = {http://hdl.handle.net/11356/1035}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)}, issn = {2820-4042}, year = {2013} } """ _DESCRIPTION = """\ The ccGigafida corpus contains a subsample of the Gigafida corpus. The Gigafida corpus is an extensive collection of Slovene text of various genres, from daily newspapers, magazines, all kinds of books (fiction, non-fiction, textbooks), web pages, transcriptions of parliamentary debates and similar. """ _HOMEPAGE = "http://eng.slovenscina.eu/korpusi/proste-zbirke" _LICENSE = "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)" _URLS = { "ccGigafida": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1035/ccGigafidaV1_0.zip" } class CcGigafida(datasets.GeneratorBasedBuilder): """(A publicly available subsample of) a reference corpus of Slovene texts.""" VERSION = datasets.Version("1.0.0") def _info(self): features = datasets.Features( { "id_doc": datasets.Value("string"), "doc_title": datasets.Value("string"), "authors": datasets.Sequence(datasets.Value("string")), "publish_date": datasets.Value("string"), "publisher": datasets.Value("string"), "genres": datasets.Sequence(datasets.Value("string")), "doc_tokenized": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))), "doc_lemmas": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))), "doc_msds": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))), "doc_string": datasets.Sequence(datasets.Sequence(datasets.Value("string"))), "id_sents": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): # Allow user to specify path to the full Gigafida directory: `load_dataset(..., data_dir=...)` if dl_manager.manual_dir is not None: data_dir = dl_manager.manual_dir else: urls = _URLS["ccGigafida"] data_dir = dl_manager.download_and_extract(urls) data_dir = os.path.join(data_dir, "ccGigafidaV1_0") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir} ) ] def _generate_examples(self, data_dir): GENRE_MAPPING = { "SSJ.T": "tisk", "SSJ.T.K": "tisk/knjižno", "SSJ.T.K.L": "tisk/knjižno/leposlovno", "SSJ.T.K.S": "tisk/knjižno/strokovno", "SSJ.T.P": "tisk/periodično", "SSJ.T.P.C": "tisk/periodično/časopis", "SSJ.T.P.R": "tisk/periodično/revija", "SSJ.T.D": "tisk/drugo", "SSJ.I": "internet" } # genres are prefixed by "ssj:" in Gigafida 2.0 for genre, description in deepcopy(GENRE_MAPPING).items(): GENRE_MAPPING[f"ssj:{genre}"] = description # Recursively search for xml files in subdirectories all_files = [os.path.join(data_dir, file_name) for file_name in glob.glob(os.path.join(data_dir, "**", "*.xml"), recursive=True) if os.path.isfile(os.path.join(data_dir, file_name))] all_files = sorted(all_files) # fix order for _idx_file, file_path in enumerate(all_files): curr_doc = ET.parse(file_path) root = curr_doc.getroot() NAMESPACE = namespace(root) id_doc = root.attrib[f"{XML_NAMESPACE}id"] # Document metadata bibl_el = root.find(f".//{NAMESPACE}bibl") doc_title = bibl_el.find(f"{NAMESPACE}title").text.strip() authors = list(map(lambda _tag: _tag.text.strip(), bibl_el.findall(f"{NAMESPACE}author"))) publish_date = bibl_el.find(f"{NAMESPACE}date").text.strip() publisher = bibl_el.find(f"{NAMESPACE}publisher").text.strip() category_tags = root.findall(f".//{NAMESPACE}catRef") genres = [] for _tag in category_tags: # in ccGigafida, the genres are noted with a "#" prefix __tag = _tag.attrib["target"][1:] if _tag.attrib["target"].startswith("#") else _tag.attrib["target"] mapped_tag = GENRE_MAPPING.get(__tag, None) # In addition to the genre of the document, there is sometimes a category assigned by the deduplication tool (dedup:nodup) if mapped_tag is None: continue genres.append(mapped_tag) # Tokenized and raw string version - raw string version preserves spaces body_tag = root.find(f".//{NAMESPACE}body") tokenized_doc, doc_str = [], [] doc_sent_ids = [] doc_msds, doc_lemmas = [], [] for para_tag in body_tag.findall(f".//{NAMESPACE}p"): id_para = para_tag.attrib[f"{XML_NAMESPACE}id"] tokenized_para, para_str = [], [] para_msds, para_lemmas = [], [] para_sent_ids = [] for _idx_sent, sent_tag in enumerate(para_tag.findall(f".//{NAMESPACE}s")): # ccGigafida does not have sentence IDs: # construct ID by taking the paragraph ID + their index in the paragraph id_sent = sent_tag.attrib.get(f"{XML_NAMESPACE}id", None) if id_sent is None: id_sent = f"{id_para}.{_idx_sent}" tokenized_sent, str_sent = [], [] msd_tags, lemmas = [], [] for child_tag in sent_tag: tag_str = child_tag.tag[len(NAMESPACE):] if tag_str not in {"w", "S", "c", "pc"}: logging.warning(f"Found unexpected tag in a sentence: '{tag_str}', skipping it.") continue # Tag for whitespace in ccGigafida if tag_str == "S": str_sent.append(" ") # Tag for: # - single-letter characters in ccGigafida; # - whitespace in Gigafida elif tag_str == "c": str_sent.append(child_tag.text) if child_tag.text != " ": tokenized_sent.append(child_tag.text) msd_tags.append(child_tag.attrib["ana"][len("mte:"):] if "ana" in child_tag.attrib else "") lemmas.append(child_tag.text) # word or punctuation character else: str_sent.append(child_tag.text) tokenized_sent.append(child_tag.text) msd_tags.append(child_tag.attrib["ana"][len("mte:"):] if "ana" in child_tag.attrib else child_tag.attrib["msd"]) lemmas.append(child_tag.attrib["lemma"] if "lemma" in child_tag.attrib else child_tag.text) str_sent = "".join(str_sent) tokenized_para.append(tokenized_sent) para_str.append(str_sent) para_sent_ids.append(id_sent) para_msds.append(msd_tags) para_lemmas.append(lemmas) tokenized_doc.append(tokenized_para) doc_str.append(para_str) doc_sent_ids.append(para_sent_ids) doc_msds.append(para_msds) doc_lemmas.append(para_lemmas) yield _idx_file, { "id_doc": id_doc, "doc_title": doc_title, "authors": authors, "publish_date": publish_date, "publisher": publisher, "genres": genres, "doc_tokenized": tokenized_doc, "doc_lemmas": doc_lemmas, "doc_msds": doc_msds, "doc_string": doc_str, "id_sents": doc_sent_ids }