Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Spanish
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
File size: 7,310 Bytes
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# 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"
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