# 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 brat_parse_to_bigbio_kb from .bigbiohub import remove_prefix _DATASETNAME = "bionlp_st_2019_bb" _DISPLAYNAME = "BioNLP 2019 BB" _SOURCE_VIEW_NAME = "source" _UNIFIED_VIEW_NAME = "bigbio" _LANGUAGES = ["English"] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{bossy-etal-2019-bacteria, title = "Bacteria Biotope at {B}io{NLP} Open Shared Tasks 2019", author = "Bossy, Robert and Del{\'e}ger, Louise and Chaix, Estelle and Ba, Mouhamadou and N{\'e}dellec, Claire", booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-5719", doi = "10.18653/v1/D19-5719", pages = "121--131", abstract = "This paper presents the fourth edition of the Bacteria Biotope task at BioNLP Open Shared Tasks 2019. The task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts, and the characterization of these entities with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on biodiversity for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, and the challenge organization. We also provide an analysis of the results obtained by participants, and inspect the evolution of the results since the last edition in 2016.", } """ _DESCRIPTION = """\ The task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts, and the characterization of these entities with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on biodiversity for fundamental research and applications in microbiology. """ _HOMEPAGE = "https://sites.google.com/view/bb-2019/dataset" _LICENSE = "License information unavailable" _SUBTASKS = ["norm", "norm+ner", "rel", "rel+ner", "kb", "kb+ner"] _FILENAMES = ["train", "dev", "test"] _URLs = { subtask: { filename: f"data/{subtask}/BioNLP-OST-2019_BB-{subtask}_{filename}.zip" for filename in _FILENAMES } for subtask in _SUBTASKS } _SUPPORTED_TASKS = [ Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION, Tasks.RELATION_EXTRACTION, ] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class bionlp_st_2019_bb(datasets.GeneratorBasedBuilder): """This dataset is the fourth edition of the Bacteria Biotope task at BioNLP Open Shared Tasks 2019""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="bionlp_st_2019_bb_norm_source", version=SOURCE_VERSION, description="bionlp_st_2019_bb entity normalization source schema", schema="source", subset_id="bionlp_st_2019_bb", ), BigBioConfig( name="bionlp_st_2019_bb_norm+ner_source", version=SOURCE_VERSION, description="bionlp_st_2019_bb entity recognition and normalization source schema", schema="source", subset_id="bionlp_st_2019_bb", ), BigBioConfig( name="bionlp_st_2019_bb_rel_source", version=SOURCE_VERSION, description="bionlp_st_2019_bb relation extraction source schema", schema="source", subset_id="bionlp_st_2019_bb", ), BigBioConfig( name="bionlp_st_2019_bb_rel+ner_source", version=SOURCE_VERSION, description="bionlp_st_2019_bb entity recognition and relation extraction source schema", schema="source", subset_id="bionlp_st_2019_bb", ), BigBioConfig( name="bionlp_st_2019_bb_kb_source", version=SOURCE_VERSION, description="bionlp_st_2019_bb entity normalization and relation extraction source schema", schema="source", subset_id="bionlp_st_2019_bb", ), BigBioConfig( name="bionlp_st_2019_bb_kb+ner_source", version=SOURCE_VERSION, description="bionlp_st_2019_bb entity recognition and normalization and relation extraction source schema", schema="source", subset_id="bionlp_st_2019_bb", ), BigBioConfig( name="bionlp_st_2019_bb_bigbio_kb", version=BIGBIO_VERSION, description="bionlp_st_2019_bb BigBio schema", schema="bigbio_kb", subset_id="bionlp_st_2019_bb", ), ] DEFAULT_CONFIG_NAME = "bionlp_st_2019_bb_kb+ner_source" 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"), "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 } ], }, ) 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]: subtask = self.config.name.split("_")[4] if subtask == "bigbio": subtask = "kb+ner" my_urls = _URLs[subtask] data_files = dl_manager.download_and_extract(my_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["dev"])}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"data_files": dl_manager.iter_files(data_files["test"])}, ), ] 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 = self.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(self.parse_brat_file(txt_file)) example["id"] = str(guid) yield guid, example guid += 1 else: raise ValueError(f"Invalid config: {self.config.name}") def parse_brat_file( self, txt_file: Path, annotation_file_suffixes: List[str] = None, parse_notes: bool = False, ) -> Dict: """ Parse a brat file into the schema defined below. `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt' Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files, e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'. Will include annotator notes, when `parse_notes == True`. brat_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" } ], ### OPTIONAL: Only included when `parse_notes == True` "notes": [ # # lines in brat { "id": datasets.Value("string"), "type": datasets.Value("string"), "ref_id": datasets.Value("string"), "text": datasets.Value("string"), } ], }, ) """ example = {} example["document_id"] = txt_file.with_suffix("").name with txt_file.open(encoding="utf-8") as f: if self.config.schema == "bigbio_kb": example["text"] = f.read().replace("\u00A0", " ").replace("\n", " ") else: example["text"] = f.read() # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes # for event extraction if annotation_file_suffixes is None: annotation_file_suffixes = [".a1", ".a2", ".ann"] if len(annotation_file_suffixes) == 0: raise AssertionError( "At least one suffix for the to-be-read annotation files should be given!" ) ann_lines = [] for suffix in annotation_file_suffixes: annotation_file = txt_file.with_suffix(suffix) try: with annotation_file.open(encoding="utf8") as f: ann_lines.extend(f.readlines()) except Exception: continue example["text_bound_annotations"] = [] example["events"] = [] example["relations"] = [] example["equivalences"] = [] example["attributes"] = [] example["normalizations"] = [] if parse_notes: example["notes"] = [] for line in ann_lines: line = line.strip() if not line: continue if line.startswith("T"): # Text bound ann = {} fields = line.split("\t") ann["id"] = fields[0] ann["type"] = fields[1].split()[0] if ann["type"] in ["Title", "Paragraph"]: continue ann["offsets"] = [] span_str = remove_prefix(fields[1], (ann["type"] + " ")) text = fields[2] for span in span_str.split(";"): start, end = span.split() ann["offsets"].append([int(start), int(end)]) # Heuristically split text of discontiguous entities into chunks ann["text"] = [] if len(ann["offsets"]) > 1: i = 0 for start, end in ann["offsets"]: chunk_len = end - start if self.config.schema == "bigbio_kb": ann["text"].append( text[i : chunk_len + i].replace("\u00A0", " ") ) else: ann["text"].append(text[i : chunk_len + i]) i += chunk_len while i < len(text) and text[i] == " ": i += 1 else: if self.config.schema == "bigbio_kb": ann["text"] = [text.replace("\u00A0", " ")] else: ann["text"] = [text] example["text_bound_annotations"].append(ann) elif line.startswith("E"): ann = {} fields = line.split("\t") ann["id"] = fields[0] ann["type"], ann["trigger"] = fields[1].split()[0].split(":") ann["arguments"] = [] for role_ref_id in fields[1].split()[1:]: argument = { "role": (role_ref_id.split(":"))[0], "ref_id": (role_ref_id.split(":"))[1], } ann["arguments"].append(argument) example["events"].append(ann) elif line.startswith("R"): ann = {} fields = line.split("\t") ann["id"] = fields[0] ann["type"] = fields[1].split()[0] ann["head"] = { "role": fields[1].split()[1].split(":")[0], "ref_id": fields[1].split()[1].split(":")[1], } ann["tail"] = { "role": fields[1].split()[2].split(":")[0], "ref_id": fields[1].split()[2].split(":")[1], } example["relations"].append(ann) # '*' seems to be the legacy way to mark equivalences, # but I couldn't find any info on the current way # this might have to be adapted dependent on the brat version # of the annotation elif line.startswith("*"): ann = {} fields = line.split("\t") ann["id"] = fields[0] ann["ref_ids"] = fields[1].split()[1:] example["equivalences"].append(ann) elif line.startswith("A") or line.startswith("M"): ann = {} fields = line.split("\t") ann["id"] = fields[0] info = fields[1].split() ann["type"] = info[0] ann["ref_id"] = info[1] if len(info) > 2: ann["value"] = info[2] else: ann["value"] = "" example["attributes"].append(ann) elif line.startswith("N"): ann = {} fields = line.split("\t") ann["id"] = fields[0] info = fields[1].split() ann["ref_id"] = info[1].split(":")[-1] ann["resource_name"] = info[0] ann["cuid"] = "".join(info[2].split(":")[1:]) example["normalizations"].append(ann) elif parse_notes and line.startswith("#"): ann = {} fields = line.split("\t") ann["id"] = fields[0] ann["text"] = fields[2] info = fields[1].split() ann["type"] = info[0] ann["ref_id"] = info[1] example["notes"].append(ann) return example