# 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. """ The identification of linguistic expressions referring to entities of interest in molecular biology such as proteins, genes and cells is a fundamental task in biomolecular text mining. The GENIA technical term annotation covers the identification of physical biological entities as well as other important terms. The corpus annotation covers the full 1,999 abstracts of the primary GENIA corpus. """ import xml.etree.ElementTree as ET from itertools import count from typing import Dict, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{10.5555/1289189.1289260, author = {Ohta, Tomoko and Tateisi, Yuka and Kim, Jin-Dong}, title = {The GENIA Corpus: An Annotated Research Abstract Corpus in Molecular Biology Domain}, year = {2002}, publisher = {Morgan Kaufmann Publishers Inc.}, address = {San Francisco, CA, USA}, booktitle = {Proceedings of the Second International Conference on Human Language Technology Research}, pages = {82–86}, numpages = {5}, location = {San Diego, California}, series = {HLT '02} } @article{Kim2003GENIAC, title={GENIA corpus - a semantically annotated corpus for bio-textmining}, author={Jin-Dong Kim and Tomoko Ohta and Yuka Tateisi and Junichi Tsujii}, journal={Bioinformatics}, year={2003}, volume={19 Suppl 1}, pages={ i180-2 } } @inproceedings{10.5555/1567594.1567610, author = {Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel}, title = {Introduction to the Bio-Entity Recognition Task at JNLPBA}, year = {2004}, publisher = {Association for Computational Linguistics}, address = {USA}, booktitle = {Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications}, pages = {70–75}, numpages = {6}, location = {Geneva, Switzerland}, series = {JNLPBA '04} } """ _DATASETNAME = "genia_term_corpus" _DISPLAYNAME = "GENIA Term Corpus" _DESCRIPTION = """\ The identification of linguistic expressions referring to entities of interest in molecular biology such as proteins, genes and cells is a fundamental task in biomolecular text mining. The GENIA technical term annotation covers the identification of physical biological entities as well as other important terms. The corpus annotation covers the full 1,999 abstracts of the primary GENIA corpus. """ _HOMEPAGE = "http://www.geniaproject.org/genia-corpus/term-corpus" _LICENSE = 'GENIA Project License for Annotated Corpora' _URLS = { _DATASETNAME: "http://www.nactem.ac.uk/GENIA/current/GENIA-corpus/Term/GENIAcorpus3.02.tgz", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] _SOURCE_VERSION = "3.0.2" _BIGBIO_VERSION = "1.0.0" class GeniaTermCorpusDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="genia_term_corpus_source", version=SOURCE_VERSION, description="genia_term_corpus source schema", schema="source", subset_id="genia_term_corpus", ), BigBioConfig( name="genia_term_corpus_bigbio_kb", version=BIGBIO_VERSION, description="genia_term_corpus BigBio schema", schema="bigbio_kb", subset_id="genia_term_corpus", ), ] DEFAULT_CONFIG_NAME = "genia_term_corpus_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "title": [ { "text": datasets.Value("string"), "entities": [ { "text": datasets.Value("string"), "lex": datasets.Value("string"), "sem": datasets.Value("string"), } ], } ], "abstract": [ { "text": datasets.Value("string"), "entities": [ { "text": datasets.Value("string"), "lex": datasets.Value("string"), "sem": 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 = dl_manager.download(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "archive": dl_manager.iter_archive(data_dir), "data_path": "GENIA_term_3.02/GENIAcorpus3.02.xml", }, ), ] def _generate_examples(self, archive, data_path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" uid = count(0) for path, file in archive: if path == data_path: for key, example in enumerate(iterparse_genia(file)): if self.config.schema == "source": yield key, example elif self.config.schema == "bigbio_kb": yield key, parse_genia_to_bigbio(example, uid) def iterparse_genia(file): # ontology = None for _, element in ET.iterparse(file): # if element.tag == "import": # ontology = {"name": element.get("resource"), "prefix": element.get("prefix")} if element.tag == "article": bibliomisc = element.find("articleinfo/bibliomisc").text document_id = parse_genia_bibliomisc(bibliomisc) title = element.find("title") title_sentences = parse_genia_sentences(title) abstract = element.find("abstract") abstract_sentences = parse_genia_sentences(abstract) yield { "document_id": document_id, "title": title_sentences, "abstract": abstract_sentences, } def parse_genia_sentences(passage): sentences = [] for sentence in passage.iter(tag="sentence"): text = "".join(sentence.itertext()) entities = [] for entity in sentence.iter(tag="cons"): # constituent entity_lex = entity.get("lex", "") entity_sem = parse_genia_sem(entity.get("sem", "")) entity_text = "".join(entity.itertext()) entities.append({"text": entity_text, "lex": entity_lex, "sem": entity_sem}) sentences.append( { "text": text, "entities": entities, } ) return sentences def parse_genia_bibliomisc(bibliomisc): """Remove 'MEDLINE:' from 'MEDLINE:96055286'.""" return bibliomisc.replace("MEDLINE:", "") if ":" in bibliomisc else bibliomisc def parse_genia_sem(sem): return sem.replace("G#", "") if "G#" in sem else sem def parse_genia_to_bigbio(example, uid): document = { "id": next(uid), "document_id": example["document_id"], "passages": list(generate_bigbio_passages(example, uid)), "entities": list(generate_bigbio_entities(example, uid)), "events": [], "coreferences": [], "relations": [], } return document def parse_genia_to_bigbio_passage(passage, uid, type="", offset=0): text = " ".join(sentence["text"] for sentence in passage) new_offset = offset + len(text) return { "id": next(uid), "type": type, "text": [text], "offsets": [[offset, new_offset]], }, new_offset + 1 def generate_bigbio_passages(example, uid): offset = 0 for type in ["title", "abstract"]: passage, offset = parse_genia_to_bigbio_passage( example[type], uid, type=type, offset=offset ) yield passage def parse_genia_to_bigbio_entity(entity, uid, text="", relative_offset=0, offset=0): try: relative_offset = text.index(entity["text"], relative_offset) except ValueError: # Skip duplicated annotations: # tumour cells return None, None new_relative_offset = relative_offset + len(entity["text"]) return { "id": next(uid), "offsets": [[offset + relative_offset, offset + new_relative_offset]], "text": [entity["text"]], "type": entity["sem"], "normalized": [], }, new_relative_offset def generate_bigbio_entities(example, uid): sentence_offset = 0 for type in ["title", "abstract"]: for sentence in example[type]: relative_offsets = {} for entity in sentence["entities"]: bigbio_entity, new_relative_offset = parse_genia_to_bigbio_entity( entity, uid, text=sentence["text"], relative_offset=relative_offsets.get( (entity["text"], entity["lex"], entity["sem"]), 0 ), offset=sentence_offset, ) if bigbio_entity: relative_offsets[ (entity["text"], entity["lex"], entity["sem"]) ] = new_relative_offset yield bigbio_entity sentence_offset += len(sentence["text"]) + 1