Datasets:
ehealth_kd

	# 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.
	"""The eHealth-KD 2020 Corpus."""


	import datasets


	_CITATION = """\
	@inproceedings{overview_ehealthkd2020,
	author = {Piad{-}Morffis, Alejandro and
	Guti{\'{e}}rrez, Yoan and
	Cañizares-Diaz, Hian and
	Estevez{-}Velarde, Suilan and
	Almeida{-}Cruz, Yudivi{\'{a}}n and
	Muñoz, Rafael and
	Montoyo, Andr{\'{e}}s},
	title = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020},
	booktitle = ,
	year = {2020},
	}
	"""

	_DESCRIPTION = """\
	Dataset of the eHealth Knowledge Discovery Challenge at IberLEF 2020. It is designed for
	the identification of semantic entities and relations in Spanish health documents.
	"""

	_HOMEPAGE = "https://knowledge-learning.github.io/ehealthkd-2020/"

	_LICENSE = "https://creativecommons.org/licenses/by-nc-sa/4.0/"

	_URL = "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/"
	_TRAIN_DIR = "training/"
	_DEV_DIR = "development/main/"
	_TEST_DIR = "testing/scenario3-taskB/"
	_TEXT_FILE = "scenario.txt"
	_ANNOTATIONS_FILE = "scenario.ann"


	class EhealthKD(datasets.GeneratorBasedBuilder):
	"""The eHealth-KD 2020 Corpus."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="ehealth_kd", version=VERSION, description="eHealth-KD Corpus"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"entities": [
	{
	"ent_id": datasets.Value("string"),
	"ent_text": datasets.Value("string"),
	"ent_label": datasets.ClassLabel(names=["Concept", "Action", "Predicate", "Reference"]),
	"start_character": datasets.Value("int32"),
	"end_character": datasets.Value("int32"),
	}
	],
	"relations": [
	{
	"rel_id": datasets.Value("string"),
	"rel_label": datasets.ClassLabel(
	names=[
	"is-a",
	"same-as",
	"has-property",
	"part-of",
	"causes",
	"entails",
	"in-time",
	"in-place",
	"in-context",
	"subject",
	"target",
	"domain",
	"arg",
	]
	),
	"arg1": datasets.Value("string"),
	"arg2": datasets.Value("string"),
	}
	],
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	k: [f"{_URL}{v}{_TEXT_FILE}", f"{_URL}{v}{_ANNOTATIONS_FILE}"]
	for k, v in zip(["train", "dev", "test"], [_TRAIN_DIR, _DEV_DIR, _TEST_DIR])
	}

	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"txt_path": downloaded_files["train"][0], "ann_path": downloaded_files["train"][1]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"txt_path": downloaded_files["dev"][0], "ann_path": downloaded_files["dev"][1]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"txt_path": downloaded_files["test"][0], "ann_path": downloaded_files["test"][1]},
	),
	]

	def _generate_examples(self, txt_path, ann_path):
	"""Yields examples."""
	with open(txt_path, encoding="utf-8") as txt_file, open(ann_path, encoding="utf-8") as ann_file:
	_id = 0
	entities = []
	relations = []

	annotations = ann_file.readlines()
	last = annotations[-1]

	# Create a variable to keep track of the last annotation (entity or relation) to know when a sentence is fully annotated
	# In the annotations file, the entities are before the relations
	last_annotation = ""

	for annotation in annotations:
	if annotation == last:
	sentence = txt_file.readline().strip()
	yield _id, {"sentence": sentence, "entities": entities, "relations": relations}

	if annotation.startswith("T"):
	if last_annotation == "relation":
	sentence = txt_file.readline().strip()
	yield _id, {"sentence": sentence, "entities": entities, "relations": relations}
	_id += 1
	entities = []
	relations = []

	ent_id, mid, ent_text = annotation.strip().split("\t")
	ent_label, spans = mid.split(" ", 1)
	start_character = spans.split(" ")[0]
	end_character = spans.split(" ")[-1]

	entities.append(
	{
	"ent_id": ent_id,
	"ent_text": ent_text,
	"ent_label": ent_label,
	"start_character": start_character,
	"end_character": end_character,
	}
	)

	last_annotation = "entity"

	else:
	rel_id, rel_label, arg1, arg2 = annotation.strip().split()
	if annotation.startswith("R"):
	arg1 = arg1.split(":")[1]
	arg2 = arg2.split(":")[1]

	relations.append({"rel_id": rel_id, "rel_label": rel_label, "arg1": arg1, "arg2": arg2})

	last_annotation = "relation"