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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.
import glob
import os
import re
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_DATASETNAME = "twadrl"
_DISPLAYNAME = "TwADR-L"
_LANGUAGES = ['English']
_PUBMED = False
_LOCAL = False
_CITATION = """
@inproceedings{limsopatham-collier-2016-normalising,
title = "Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation",
author = "Limsopatham, Nut and
Collier, Nigel",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-1096",
doi = "10.18653/v1/P16-1096",
pages = "1014--1023",
}
"""
_DESCRIPTION = """
The TwADR-L dataset contains medical concepts written on social media (Twitter) \
mapped to how they are formally written in medical ontologies (SIDER 4). \
"""
_HOMEPAGE = "https://zenodo.org/record/55013"
_LICENSE = 'Creative Commons Attribution 4.0 International'
_URLs = "https://zenodo.org/record/55013/files/datasets.zip"
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class TwADRL(datasets.GeneratorBasedBuilder):
"""TwADR-L: Dataset for Normalising Medical Concepts on Twitter."""
DEFAULT_CONFIG_NAME = "twadrl_source"
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="twadrl_source",
version=SOURCE_VERSION,
description="TwADR-L source schema",
schema="source",
subset_id="twadrl",
),
BigBioConfig(
name="twadrl_bigbio_kb",
version=BIGBIO_VERSION,
description="TwADR-L simplified BigBio schema",
schema="bigbio_kb",
subset_id="twadrl",
),
]
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"cui": datasets.Value("string"),
"medical_concept": datasets.Value("string"),
"social_media_text": datasets.Value("string"),
}
)
elif self.config.schema == "bigbio_kb":
features = kb_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URLs)
dataset_dir = os.path.join(dl_dir, "datasets", "TwADR-L")
# dataset supports k-folds
splits_names = ["train", "validation", "test"]
fold_ids = range(10)
return [
datasets.SplitGenerator(
name=f"{split_name}_{fold_id}",
gen_kwargs={
"filepath": os.path.join(dataset_dir, f"TwADR-L.fold-{fold_id}.{split_name}.txt"),
"split_id": f"{split_name}_{fold_id}",
},
)
for split_name in splits_names
for fold_id in fold_ids
]
def _generate_examples(self, filepath, split_id):
with open(filepath, "r", encoding="latin-1") as f:
for i, line in enumerate(f):
guid = f"{split_id}_{i}"
cui, medical_concept, social_media_text = line.strip().split("\t")
if self.config.schema == "source":
yield guid, {
"cui": cui,
"medical_concept": medical_concept,
"social_media_text": social_media_text,
}
elif self.config.schema == "bigbio_kb":
text_type = "social_media_text"
offset = (0, len(social_media_text))
yield guid, {
"id": guid,
"document_id": guid,
"passages": [
{
"id": f"{guid}_passage",
"type": text_type,
"text": [social_media_text],
"offsets": [offset],
}
],
"entities": [
{
"id": f"{guid}_entity",
"type": text_type,
"text": [social_media_text],
"offsets": [offset],
"normalized": [{"db_name": "SIDER 4", "db_id": cui}],
}
],
"events": [],
"coreferences": [],
"relations": [],
}
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