# 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. from pathlib import Path from typing import Dict, List 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 _DATASETNAME = "bionlp_st_2011_ge" _DISPLAYNAME = "BioNLP 2011 GE" _SOURCE_VIEW_NAME = "source" _UNIFIED_VIEW_NAME = "bigbio" _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{10.5555/2107691.2107693, author = {Kim, Jin-Dong and Wang, Yue and Takagi, Toshihisa and Yonezawa, Akinori}, title = {Overview of Genia Event Task in BioNLP Shared Task 2011}, year = {2011}, isbn = {9781937284091}, publisher = {Association for Computational Linguistics}, address = {USA}, abstract = {The Genia event task, a bio-molecular event extraction task, is arranged as one of the main tasks of BioNLP Shared Task 2011. As its second time to be arranged for community-wide focused efforts, it aimed to measure the advance of the community since 2009, and to evaluate generalization of the technology to full text papers. After a 3-month system development period, 15 teams submitted their performance results on test cases. The results show the community has made a significant advancement in terms of both performance improvement and generalization.}, booktitle = {Proceedings of the BioNLP Shared Task 2011 Workshop}, pages = {7–15}, numpages = {9}, location = {Portland, Oregon}, series = {BioNLP Shared Task '11} } """ _DESCRIPTION = """\ The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE. The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein" without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components, are not differentiated from each other, and their type is given as "Entity". """ _HOMEPAGE = "https://sites.google.com/site/bionlpst/bionlp-shared-task-2011/genia-event-extraction-genia" _LICENSE = 'Creative Commons Attribution 3.0 Unported' _URLs = { "train": "data/train.zip", "validation": "data/devel.zip", "test": "data/test.zip", } _SUPPORTED_TASKS = [ Tasks.EVENT_EXTRACTION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.COREFERENCE_RESOLUTION, ] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class bionlp_st_2011_ge(datasets.GeneratorBasedBuilder): """The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="bionlp_st_2011_ge_source", version=SOURCE_VERSION, description="bionlp_st_2011_ge source schema", schema="source", subset_id="bionlp_st_2011_ge", ), BigBioConfig( name="bionlp_st_2011_ge_bigbio_kb", version=BIGBIO_VERSION, description="bionlp_st_2011_ge BigBio schema", schema="bigbio_kb", subset_id="bionlp_st_2011_ge", ), ] DEFAULT_CONFIG_NAME = "bionlp_st_2011_ge_source" _ROLE_MAPPING = { "Theme2": "Theme", "Theme3": "Theme", "Theme4": "Theme", "Site2": "Site", } def _info(self): """ - `features` defines the schema of the parsed data set. The schema depends on the chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the canonical KB-task schema defined in `biomedical/schemas/kb.py`. """ if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "document_id": datasets.Value("string"), "text": datasets.Value("string"), "text_bound_annotations": [ # T line in brat, e.g. type or event trigger { "offsets": datasets.Sequence([datasets.Value("int32")]), "text": datasets.Sequence(datasets.Value("string")), "type": datasets.Value("string"), "id": datasets.Value("string"), } ], "events": [ # E line in brat { "trigger": datasets.Value( "string" ), # refers to the text_bound_annotation of the trigger, "id": datasets.Value("string"), "type": datasets.Value("string"), "arguments": datasets.Sequence( { "role": datasets.Value("string"), "ref_id": datasets.Value("string"), } ), } ], "relations": [ # R line in brat { "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"), } ], "equivalences": [ # Equiv line in brat { "id": datasets.Value("string"), "ref_ids": datasets.Sequence(datasets.Value("string")), } ], "attributes": [ # M or A lines in brat { "id": datasets.Value("string"), "type": datasets.Value("string"), "ref_id": datasets.Value("string"), "value": datasets.Value("string"), } ], "normalizations": [ # N lines in brat { "id": datasets.Value("string"), "type": datasets.Value("string"), "ref_id": datasets.Value("string"), "resource_name": datasets.Value( "string" ), # Name of the resource, e.g. "Wikipedia" "cuid": datasets.Value( "string" ), # ID in the resource, e.g. 534366 "text": datasets.Value( "string" ), # Human readable description/name of the entity, e.g. "Barack Obama" } ], }, ) 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: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: data_files = dl_manager.download_and_extract(_URLs) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data_files": dl_manager.iter_files(data_files["train"])}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"data_files": dl_manager.iter_files(data_files["validation"])}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"data_files": dl_manager.iter_files(data_files["test"])}, ), ] def _standardize_arguments_roles(self, kb_example: Dict) -> Dict: for event in kb_example["events"]: for argument in event["arguments"]: role = argument["role"] argument["role"] = self._ROLE_MAPPING.get(role, role) return kb_example def _generate_examples(self, data_files: Path): if self.config.schema == "source": guid = 0 for data_file in data_files: txt_file = Path(data_file) if txt_file.suffix != ".txt": continue example = parse_brat_file(txt_file) example["id"] = str(guid) yield guid, example guid += 1 elif self.config.schema == "bigbio_kb": guid = 0 for data_file in data_files: txt_file = Path(data_file) if txt_file.suffix != ".txt": continue example = brat_parse_to_bigbio_kb( parse_brat_file(txt_file) ) example = self._standardize_arguments_roles(example) example["id"] = str(guid) yield guid, example guid += 1 else: raise ValueError(f"Invalid config: {self.config.name}")