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import logging
import os
import re
import xml.etree.ElementTree as ET
from typing import Optional
import datasets
_CITATION = """\
@misc{solar3.0,
title = {Developmental corpus {\v S}olar 3.0},
author = {Arhar Holdt, {\v S}pela and Rozman, Tadeja and Stritar Ku{\v c}uk, Mojca and Krek, Simon and Krap{\v s} Vodopivec, Irena and Stabej, Marko and Pori, Eva and Goli, Teja and Lavri{\v c}, Polona and Laskowski, Cyprian and Kocjan{\v c}i{\v c}, Polonca and Klemenc, Bojan and Krsnik, Luka and Kosem, Iztok},
url = {http://hdl.handle.net/11356/1589},
note = {Slovenian language resource repository {CLARIN}.{SI}},
year = {2022}
}
"""
_DESCRIPTION = """\
Šolar is a developmental corpus of 5485 school texts (e.g., essays), written by students in Slovenian secondary schools
(age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade.
Part of the corpus (2,094 texts) is annotated with teachers' corrections using a system of labels described in the
document available at https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Smernice-za-oznacevanje-korpusa-Solar_V1.1.pdf (in Slovenian).
"""
_HOMEPAGE = "http://hdl.handle.net/11356/1589"
_LICENSE = "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
_URLS = {
"solar_tei": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Solar.TEI.zip"
}
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 ''
def resolve_element(tag_el, ne_tag: Optional[str] = "O"):
if not tag_el.tag.endswith(("w", "pc", "seg")):
logging.info(f"Skipping tag {tag_el.tag}")
return []
if tag_el.tag.endswith(("w", "pc")):
form = tag_el.text.strip()
lemma = tag_el.text.strip() if tag_el.tag.endswith("pc") else tag_el.attrib["lemma"]
msd = tag_el.attrib["ana"]
ret_ne_tag = ne_tag
id_tag = tag_el.attrib[f"{XML_NAMESPACE}id"]
return [(id_tag, form, lemma, msd, ret_ne_tag)]
# Named entities: words and punctuation nested directly below current element
elif tag_el.tag.endswith("seg"):
anns = []
ret_ne_tag = tag_el.attrib["subtype"].upper()
for curr_child in tag_el:
anns.extend(resolve_element(curr_child, ne_tag=ret_ne_tag))
return anns
def read_data(data_path):
data = {} # ID_sent -> sentence_metadata
tree = ET.parse(data_path)
root = tree.getroot()
NAMESPACE = namespace(root)
for curr_text in root.iterfind(f".//{NAMESPACE}div"):
id_text = curr_text.attrib[f"{XML_NAMESPACE}id"]
bibl_el = curr_text.find(f"{NAMESPACE}bibl")
if bibl_el is None:
text_title = "Unknown_title"
logging.warning(f"The following text does not have a 'bibl' element: {curr_text.attrib}. "
f"Setting title to 'Unknown_title'")
is_manually_validated = False
else:
text_title = bibl_el.attrib["n"]
note_el = bibl_el.find(f"{NAMESPACE}note")
is_manually_validated = note_el.text == "DA"
for idx_par, curr_par in enumerate(curr_text.iterfind(f".//{NAMESPACE}p")):
for idx_sent, curr_sent in enumerate(curr_par.iterfind(f".//{NAMESPACE}s")):
id_sent = curr_sent.attrib[f"{XML_NAMESPACE}id"]
ids, forms, lemmas, msds, nes = [], [], [], [], []
for curr_el in curr_sent:
curr_annotations = resolve_element(curr_el)
for curr_ann in curr_annotations:
ids.append(curr_ann[0])
forms.append(curr_ann[1])
lemmas.append(curr_ann[2])
msds.append(curr_ann[3])
nes.append(curr_ann[4])
data[id_sent] = {
"id_doc": id_text,
"doc_title": text_title,
"idx_par": idx_par,
"idx_sent": idx_sent,
"id_token": ids, "form": forms, "lemma": lemmas, "msd": msds, "ne_tag": nes,
"is_manually_validated": is_manually_validated
}
return data
class Solar3(datasets.GeneratorBasedBuilder):
"""Šolar is a developmental corpus of school texts (e.g., essays), annotated with metadata and (partially)
with teachers' corrections. """
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"id_doc": datasets.Value("string"),
"doc_title": datasets.Value("string"),
"is_manually_validated": datasets.Value("bool"),
"idx_src_par": datasets.Value("int32"),
"idx_src_sent": datasets.Value("int32"),
"id_src_tokens": datasets.Sequence(datasets.Value("string")),
"src_tokens": datasets.Sequence(datasets.Value("string")),
"idx_tgt_par": datasets.Value("int32"),
"idx_tgt_sent": datasets.Value("int32"),
"id_tgt_tokens": datasets.Sequence(datasets.Value("string")),
"tgt_tokens": datasets.Sequence(datasets.Value("string")),
"corrections": [
{
"idx_src": datasets.Sequence(datasets.Value("int32")),
"idx_tgt": datasets.Sequence(datasets.Value("int32")),
"corr_types": 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["solar_tei"]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"source_path": os.path.join(data_dir, "Solar.TEI", "solar-orig.xml"),
"target_path": os.path.join(data_dir, "Solar.TEI", "solar-corr.xml"),
"links_path": os.path.join(data_dir, "Solar.TEI", "solar-errs.xml")
}
)
]
def _generate_examples(self, source_path, target_path, links_path):
source_data = read_data(source_path)
target_data = read_data(target_path)
data = ET.parse(links_path)
root = data.getroot()
NAMESPACE = namespace(root)
for idx_corr, corrected_sent in enumerate(root.iterfind(f"{NAMESPACE}linkGrp")):
involved_sents = corrected_sent.attrib["corresp"].split(" ")
assert len(involved_sents) <= 2, f"The following correction has more than two sentences involved, " \
f"which the script cannot handle: {corrected_sent.attrib}"
if len(involved_sents) == 2:
# not always ordered <source> <target>, sometimes reversed
if "t" in involved_sents[0]:
id_src_sent, id_tgt_sent = list(map(lambda _s: _s[1:], involved_sents[::-1]))
else:
id_src_sent, id_tgt_sent = list(map(lambda _s: _s[1:], involved_sents))
else: # one sentence: only source or only target (usually a sentence marked as redundant or as missing)
id_src_sent, id_tgt_sent = None, None
if "t" in involved_sents[0]:
id_tgt_sent = involved_sents[0][1:]
else:
id_src_sent = involved_sents[0][1:]
id_doc, doc_title, is_manually_validated = None, None, False
src_sent_data, tgt_sent_data = {}, {}
assert id_src_sent is not None or id_tgt_sent is not None
if id_src_sent is not None:
src_sent_data = source_data[id_src_sent]
id_doc = src_sent_data["id_doc"]
doc_title = src_sent_data["doc_title"]
is_manually_validated |= src_sent_data["is_manually_validated"]
if id_tgt_sent is not None:
tgt_sent_data = target_data[id_tgt_sent]
id_doc = tgt_sent_data["id_doc"]
doc_title = tgt_sent_data["doc_title"]
is_manually_validated |= tgt_sent_data["is_manually_validated"]
corr_data = []
for token_info in corrected_sent.findall(f"{NAMESPACE}link"):
connections = token_info.attrib["target"].split(" ")
corrections = token_info.attrib["type"]
if corrections == "ID":
continue
src_inds, tgt_inds = [], []
corr_types = []
for curr_corr in corrections.split("|"):
corr_types.append(curr_corr)
for curr_tok in connections:
# Token IDs have an index at the end, but it is 1-based; convert it to 0-based
idx_tok = int(curr_tok.split(".")[-1]) - 1
if "t" in curr_tok: # target token
tgt_inds.append(idx_tok)
else: # source token
src_inds.append(idx_tok)
corr_data.append({"idx_src": src_inds, "idx_tgt": tgt_inds, "corr_types": corr_types})
yield idx_corr, {
"id_doc": id_doc[:-1], # doc ID without the "s" or "t" info
"doc_title": doc_title,
"is_manually_validated": is_manually_validated,
"idx_src_par": src_sent_data.get("idx_par", -1),
"idx_src_sent": src_sent_data.get("idx_sent", -1),
"id_src_tokens": src_sent_data.get("id_token", []),
"src_tokens": src_sent_data.get("form", []),
"idx_tgt_par": tgt_sent_data.get("idx_par", -1),
"idx_tgt_sent": tgt_sent_data.get("idx_sent", -1),
"id_tgt_tokens": tgt_sent_data.get("id_token", []),
"tgt_tokens": tgt_sent_data.get("form", []),
"corrections": corr_data
}
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