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