Datasets:
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import os
import re
import xml.etree.ElementTree as ET
import datasets
_CITATION = """\
@misc{janes_tag,
title = {{CMC} training corpus Janes-Tag 3.0},
author = {Lenardi{\v c}, Jakob and {\v C}ibej, Jaka and Arhar Holdt, {\v S}pela and Erjavec, Toma{\v z} and Fi{\v s}er, Darja and Ljube{\v s}i{\'c}, Nikola and Zupan, Katja and Dobrovoljc, Kaja},
url = {http://hdl.handle.net/11356/1732},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2022}
}
"""
_DESCRIPTION = """\
Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication (CMC) consisting of mostly tweets
but also blogs, forums and news comments.
"""
_HOMEPAGE = "https://nl.ijs.si/janes/"
_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
_URLS = {
"janes_tag_tei": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1732/Janes-Tag.3.0.TEI.zip"
}
XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
DEFAULT_NE = "O"
def namespace(element):
# https://stackoverflow.com/a/12946675
m = re.match(r'\{.*\}', element.tag)
return m.group(0) if m else ''
def word_info(wordlike_tag, _namespace):
if wordlike_tag.tag == f"{_namespace}c":
return None, None, None, None
if wordlike_tag.tag in {f"{_namespace}w", f"{_namespace}pc"}:
nes = None
children = list(iter(wordlike_tag))
if len(children) > 0:
# If this happens, the word contains nested words indicating its normalized form
words, lemmas, msds = [], [], []
for _child in wordlike_tag:
assert _child.tag in {f"{_namespace}w", f"{_namespace}pc"}, _child.tag
# Arbitrary words in the text have a normalized form that is formatted inconsistently and so it is
# unclear how to parse it correctly -> convention: always use information of the normalized words
if "norm" in _child.attrib:
words.append(_child.attrib["norm"].strip())
lemmas.append(_child.attrib["lemma"].strip())
msds.append(_child.attrib["ana"].strip())
else:
# These don't have linguistic annotations ¯\_(ツ)_/¯
words.append(_child.text.strip())
lemmas.append(_child.text.strip())
msds.append("UNK")
else:
words = [wordlike_tag.text.strip()]
lemmas = [wordlike_tag.attrib["lemma"].strip()]
msds = [wordlike_tag.attrib["ana"].strip()]
return words, lemmas, msds, nes
words, lemmas, msds, nes = [], [], [], []
if wordlike_tag.tag == f"{_namespace}seg":
ne_tag = wordlike_tag.attrib["subtype"].strip().upper()
if ne_tag.startswith("DERIV-"):
ne_tag = ne_tag[len("DERIV-"):]
for _child in wordlike_tag:
_child_words, _child_lemmas, _child_msds, _child_nes = word_info(_child, _namespace)
if _child_words is None:
continue
words.extend(_child_words)
lemmas.extend(_child_lemmas)
msds.extend(_child_msds)
nes = [f"B-{ne_tag}" if _i == 0 else f"I-{ne_tag}" for _i, _ in enumerate(words)]
return words, lemmas, msds, nes
class JanesTag(datasets.GeneratorBasedBuilder):
"""Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication"""
VERSION = datasets.Version("3.0.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"words": datasets.Sequence(datasets.Value("string")),
"lemmas": datasets.Sequence(datasets.Value("string")),
"msds": datasets.Sequence(datasets.Value("string")),
"nes": datasets.Sequence(datasets.Value("string"))
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS["janes_tag_tei"]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"file_path": os.path.join(data_dir, "Janes-Tag.3.0.TEI", "janes-tag.xml")}
)
]
def _generate_examples(self, file_path):
curr_doc = ET.parse(file_path)
root = curr_doc.getroot()
NAMESPACE = namespace(root)
root = root.find(f"{NAMESPACE}text").find(f"{NAMESPACE}body")
idx_ex = 0
for curr_ex in root.iterfind(f"{NAMESPACE}ab"): # anonymous block
curr_id = curr_ex.attrib[f"{XML_NAMESPACE}id"]
ex_words, ex_lemmas, ex_msds, ex_nes = [], [], [], []
for child_tag in curr_ex:
if child_tag.tag not in {f"{NAMESPACE}s", f"{NAMESPACE}c"}:
continue
if child_tag.tag == f"{NAMESPACE}c":
continue
# Iterate over elements of a <s>entence
for word_or_seg_tag in child_tag:
_words, _lemmas, _msds, _nes = word_info(word_or_seg_tag, NAMESPACE)
if _words is None:
continue
if _nes is None:
_nes = [DEFAULT_NE for _ in range(len(_words))]
ex_words.extend(_words)
ex_lemmas.extend(_lemmas)
ex_msds.extend(_msds)
ex_nes.extend(_nes)
yield idx_ex, {
"id": curr_id,
"words": ex_words,
"lemmas": ex_lemmas,
"msds": ex_msds,
"nes": ex_nes
}
idx_ex += 1
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