Datasets:
multi_booked

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""MultiBooked dataset."""

	import os
	import xml.etree.ElementTree as ET
	from collections import defaultdict
	from pathlib import Path

	import datasets


	_CITATION = """\
	@inproceedings{Barnes2018multibooked,
	author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
	title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
	booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
	year = {2018},
	month = {May},
	date = {7-12},
	address = {Miyazaki, Japan},
	publisher = {European Language Resources Association (ELRA)},
	language = {english}
	}
	"""

	_DESCRIPTION = """\
	MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

	The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
	an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
	word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
	annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
	guidelines set out in the OpeNER project.
	"""

	_HOMEPAGE = "http://hdl.handle.net/10230/33928"

	_LICENSE = "CC-BY 3.0"

	_URL = "https://github.com/jerbarnes/multibooked/archive/master.zip"


	class MultiBooked(datasets.GeneratorBasedBuilder):
	"""MultiBooked dataset."""

	VERSION = datasets.Version("0.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="ca", description="MultiBooked dataset in Catalan language."),
	datasets.BuilderConfig(name="eu", description="MultiBooked dataset in Basque language."),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.features.Sequence(
	{
	"wid": datasets.Value("string"),
	"sent": datasets.Value("string"),
	"para": datasets.Value("string"),
	"word": datasets.Value("string"),
	}
	),
	"terms": datasets.features.Sequence(
	{
	"tid": datasets.Value("string"),
	"lemma": datasets.Value("string"),
	"morphofeat": datasets.Value("string"),
	"pos": datasets.Value("string"),
	"target": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	"opinions": datasets.features.Sequence(
	{
	"oid": datasets.Value("string"),
	"opinion_holder_target": datasets.features.Sequence(datasets.Value("string")),
	"opinion_target_target": datasets.features.Sequence(datasets.Value("string")),
	"opinion_expression_polarity": datasets.features.ClassLabel(
	names=["StrongNegative", "Negative", "Positive", "StrongPositive"]
	),
	"opinion_expression_target": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"dirpath": os.path.join(data_dir, "multibooked-master", "corpora", self.config.name),
	},
	),
	]

	def _generate_examples(self, dirpath):
	for id_, filepath in enumerate(sorted(Path(dirpath).iterdir())):
	example = defaultdict(lambda: defaultdict(list))
	with open(filepath, encoding="utf-8") as f:
	for _, elem in ET.iterparse(f):
	if elem.tag == "text":
	for child in elem:
	# sometimes wid is missing in the eu configuration
	example["text"]["wid"].append(child.attrib.get("wid", ""))
	example["text"]["sent"].append(child.attrib["sent"])
	example["text"]["para"].append(child.attrib["para"])
	example["text"]["word"].append(child.text)
	elif elem.tag == "terms":
	for child in elem:
	# sometimes tid is missing in the eu configuration
	example["terms"]["tid"].append(child.attrib.get("tid", ""))
	example["terms"]["lemma"].append(child.attrib["lemma"])
	example["terms"]["morphofeat"].append(child.attrib["morphofeat"])
	example["terms"]["pos"].append(child.attrib["pos"])
	targets = []
	for target in child.findall("span/target"):
	targets.append(target.attrib["id"])
	example["terms"]["target"].append(targets)
	elif elem.tag == "opinions":
	for child in elem:
	example["opinions"]["oid"].append(child.attrib["oid"])
	# Opinion holder
	opinion_holder = child.find("opinion_holder")
	targets = []
	for target in opinion_holder.findall("span/target"):
	targets.append(target.attrib["id"])
	example["opinions"]["opinion_holder_target"].append(targets)
	# Opinion target
	opinion_target = child.find("opinion_target")
	targets = []
	for target in opinion_target.findall("span/target"):
	targets.append(target.attrib["id"])
	example["opinions"]["opinion_target_target"].append(targets)
	# Opinion expression
	opinion_expression = child.find("opinion_expression")
	example["opinions"]["opinion_expression_polarity"].append(
	opinion_expression.attrib["polarity"]
	)
	targets = []
	for target in opinion_expression.findall("span/target"):
	targets.append(target.attrib["id"])
	example["opinions"]["opinion_expression_target"].append(targets)
	yield id_, example