# 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. """ A corpus for plant and chemical entities and for the relationships between them. The corpus contains 2218 plant and chemical entities and 600 plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed abstracts. """ from pathlib import Path from typing import Dict, Iterator, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @article{choi2016corpus, title = {A corpus for plant-chemical relationships in the biomedical domain}, author = { Choi, Wonjun and Kim, Baeksoo and Cho, Hyejin and Lee, Doheon and Lee, Hyunju }, year = 2016, journal = {BMC bioinformatics}, publisher = {Springer}, volume = 17, number = 1, pages = {1--15} } """ _DATASETNAME = "pcr" _DISPLAYNAME = "PCR" _DESCRIPTION = """ A corpus for plant / herb and chemical entities and for the relationships \ between them. The corpus contains 2218 plant and chemical entities and 600 \ plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed \ abstracts. """ _HOMEPAGE = "http://210.107.182.73/plantchemcorpus.htm" _LICENSE = 'License information unavailable' _URLS = {_DATASETNAME: "http://210.107.182.73/1109_corpus_units_STformat.tar"} _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.EVENT_EXTRACTION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class PCRDataset(datasets.GeneratorBasedBuilder): """ The corpus of plant-chemical relation consists of plants / herbs and chemicals and relations between them. """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="pcr_source", version=SOURCE_VERSION, description="PCR source schema", schema="source", subset_id="pcr", ), BigBioConfig( name="pcr_fixed_source", version=SOURCE_VERSION, description="PCR (with fixed offsets) source schema", schema="source", subset_id="pcr_fixed", ), BigBioConfig( name="pcr_bigbio_kb", version=BIGBIO_VERSION, description="PCR BigBio schema", schema="bigbio_kb", subset_id="pcr", ), ] DEFAULT_CONFIG_NAME = "pcr_source" def _info(self): if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "text": datasets.Value("string"), "entities": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "offsets": datasets.Sequence([datasets.Value("int32")]), "text": datasets.Sequence(datasets.Value("string")), "normalized": [ { "db_name": datasets.Value("string"), "db_id": datasets.Value("string"), } ], } ], "events": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), # refers to the text_bound_annotation of the trigger "trigger": { "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), }, "arguments": [ { "role": datasets.Value("string"), "ref_id": 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): urls = _URLS[_DATASETNAME] data_dir = Path(dl_manager.download_and_extract(urls)) data_dir = data_dir / "1109 corpus units" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir}, ) ] def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]: if self.config.schema == "source": for file in data_dir.iterdir(): if not str(file).endswith(".txt"): continue example = parsing.parse_brat_file(file) example = parsing.brat_parse_to_bigbio_kb(example) example = self._to_source_example(example) # Three documents have incorrect offsets - fix them for fixed_source scheme if self.config.subset_id == "pcr_fixed" and example["document_id"] in [ "463", "509", "566", ]: example = self._fix_example(example) yield example["document_id"], example elif self.config.schema == "bigbio_kb": for file in data_dir.iterdir(): if not str(file).endswith(".txt"): continue example = parsing.parse_brat_file(file) example = parsing.brat_parse_to_bigbio_kb(example) document_id = example["document_id"] example["id"] = document_id # Three documents have incorrect offsets - fix them for BigBio scheme if document_id in ["463", "509", "566"]: example = self._fix_example(example) yield example["id"], example def _to_source_example(self, bigbio_example: Dict) -> Dict: """ Converts an example in BigBio-KB scheme to an example according to the source scheme """ source_example = bigbio_example.copy() source_example["text"] = bigbio_example["passages"][0]["text"][0] source_example.pop("passages", None) source_example.pop("relations", None) source_example.pop("coreferences", None) return source_example def _fix_example(self, example: Dict) -> Dict: """ Fixes by the example by adapting the offsets of the trigger word of the first event. In the official annotation data the end offset is incorrect (for 3 examples). """ first_event = example["events"][0] trigger_text = first_event["trigger"]["text"][0] offsets = first_event["trigger"]["offsets"][0] real_offsets = [offsets[0], offsets[0] + len(trigger_text)] example["events"][0]["trigger"]["offsets"] = [real_offsets] return example