Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Spanish
Size:
1K - 10K
Tags:
relation-prediction
License:
# 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.""" | |
from __future__ import absolute_import, division, print_function | |
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" | |