# coding=utf-8 # Copyright 2022 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. """ AnEM corpus is a domain- and species-independent resource manually annotated for anatomical entity mentions using a fine-grained classification system. The corpus consists of 500 documents (over 90,000 words) selected randomly from citation abstracts and full-text papers with the aim of making the corpus representative of the entire available biomedical scientific literature. The corpus annotation covers mentions of both healthy and pathological anatomical entities and contains over 3,000 annotated mentions. """ from pathlib import Path from typing import Dict, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks from .bigbiohub import parse_brat_file from .bigbiohub import brat_parse_to_bigbio_kb _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{ohta-etal-2012-open, author = {Ohta, Tomoko and Pyysalo, Sampo and Tsujii, Jun{'}ichi and Ananiadou, Sophia}, title = {Open-domain Anatomical Entity Mention Detection}, journal = {}, volume = {W12-43}, year = {2012}, url = {https://aclanthology.org/W12-4304}, doi = {}, biburl = {}, bibsource = {}, publisher = {Association for Computational Linguistics} } """ _DATASETNAME = "an_em" _DISPLAYNAME = "AnEM" _DESCRIPTION = """\ AnEM corpus is a domain- and species-independent resource manually annotated for anatomical entity mentions using a fine-grained classification system. The corpus consists of 500 documents (over 90,000 words) selected randomly from citation abstracts and full-text papers with the aim of making the corpus representative of the entire available biomedical scientific literature. The corpus annotation covers mentions of both healthy and pathological anatomical entities and contains over 3,000 annotated mentions. """ _HOMEPAGE = "http://www.nactem.ac.uk/anatomy/" _LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported' _URLS = { _DATASETNAME: "http://www.nactem.ac.uk/anatomy/data/AnEM-1.0.4.tar.gz", } _SUPPORTED_TASKS = [ Tasks.NAMED_ENTITY_RECOGNITION, Tasks.COREFERENCE_RESOLUTION, Tasks.RELATION_EXTRACTION, ] _SOURCE_VERSION = "1.0.4" _BIGBIO_VERSION = "1.0.0" class AnEMDataset(datasets.GeneratorBasedBuilder): """Anatomical Entity Mention (AnEM) corpus""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="an_em_source", version=SOURCE_VERSION, description="AnEM source schema", schema="source", subset_id="an_em", ), BigBioConfig( name="an_em_bigbio_kb", version=BIGBIO_VERSION, description="AnEM BigBio schema", schema="bigbio_kb", subset_id="an_em", ), ] DEFAULT_CONFIG_NAME = "an_em_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "text": datasets.Value("string"), "document_type": datasets.Value("string"), "text_type": datasets.Value("string"), "entities": [ { "offsets": datasets.Sequence([datasets.Value("int32")]), "text": datasets.Value("string"), "type": datasets.Value("string"), "entity_id": datasets.Value("string"), } ], "equivalences": [ { "entity_id": datasets.Value("string"), "ref_ids": datasets.Sequence(datasets.Value("string")), } ], "relations": [ { "id": datasets.Value("string"), "head": { "ref_id": datasets.Value("string"), "role": datasets.Value("string"), }, "tail": { "ref_id": datasets.Value("string"), "role": datasets.Value("string"), }, "type": datasets.Value("string"), } ], } ) elif self.config.schema == "bigbio_kb": features = kb_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" urls = _URLS[_DATASETNAME] data_dir = Path(dl_manager.download_and_extract(urls)) all_data = data_dir / "AnEM-1.0.4" / "standoff" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": all_data, "split_path": data_dir / "AnEM-1.0.4" / "development" / "train-files.list", "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": all_data, "split_path": data_dir / "AnEM-1.0.4" / "test" / "test-files.list", "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": all_data, "split_path": data_dir / "AnEM-1.0.4" / "development" / "test-files.list", "split": "dev", }, ), ] def _generate_examples(self, filepath, split_path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" with open(split_path, "r") as sp: split_list = [line.rstrip() for line in sp] if self.config.schema == "source": for file in filepath.iterdir(): # Use brat text files and consider files in the provided split list if (file.suffix != ".txt") or (file.stem not in split_list): continue brat_parsed = parse_brat_file(file) source_example = self._brat_to_source(file, brat_parsed) yield source_example["document_id"], source_example elif self.config.schema == "bigbio_kb": for file in filepath.iterdir(): # Use brat text files and consider files in the provided split list if (file.suffix != ".txt") or (file.stem not in split_list): continue brat_parsed = parse_brat_file(file) bigbio_kb_example = brat_parse_to_bigbio_kb(brat_parsed) bigbio_kb_example["id"] = bigbio_kb_example["document_id"] doc_type, text_type = self.get_document_type_and_text_type(file) bigbio_kb_example["passages"][0]["type"] = text_type yield bigbio_kb_example["id"], bigbio_kb_example def _brat_to_source(self, filepath, brat_example): """ Converts parsed brat example to source schema example """ document_type, text_type = self.get_document_type_and_text_type(filepath) source_example = { "document_id": brat_example["document_id"], "text": brat_example["text"], "document_type": document_type, "text_type": text_type, "entities": [ { "offsets": brat_entity["offsets"], "text": brat_entity["text"], "type": brat_entity["type"], "entity_id": f"{brat_example['document_id']}_{brat_entity['id']}", } for brat_entity in brat_example["text_bound_annotations"] ], "equivalences": [ { "entity_id": brat_entity["id"], "ref_ids": [ f"{brat_example['document_id']}_{ids}" for ids in brat_entity["ref_ids"] ], } for brat_entity in brat_example["equivalences"] ], "relations": [ { "id": f"{brat_example['document_id']}_{brat_entity['id']}", "head": { "ref_id": f"{brat_example['document_id']}_{brat_entity['head']['ref_id']}", "role": brat_entity["head"]["role"], }, "tail": { "ref_id": f"{brat_example['document_id']}_{brat_entity['tail']['ref_id']}", "role": brat_entity["tail"]["role"], }, "type": brat_entity["type"], } for brat_entity in brat_example["relations"] ], } return source_example def get_document_type_and_text_type(self, input_file: Path) -> Tuple[str, str]: """ Implementation used from https://github.com/bigscience-workshop/biomedical/blob/master/biodatasets/anat_em/anat_em.py Extracts the document type (PubMed(PM) or PubMedCentral (PMC)) and the respective text type (abstract for PM and sec or caption for (PMC) from the name of the given file, e.g.: PMID-9778569.txt -> ("PM", "abstract") PMC-1274342-sec-02.txt -> ("PMC", "sec") PMC-1592597-caption-02.ann -> ("PMC", "caption") """ name_parts = str(input_file.stem).split("-") if name_parts[0] == "PMID": return "PM", "abstract" elif name_parts[0] == "PMC": return "PMC", name_parts[2] else: raise AssertionError(f"Unexpected file prefix {name_parts[0]}")