coneco.py

# 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""
A dataset loading script for the Complex Named Entity Corpus (CoNECo.)

CoNECo is an annotated corpus for NER and NEN of protein-containing complexes. \
CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are normalized \
to Gene Ontology. We divided the corpus into training, development, and test sets.
"""

from pathlib import Path
from typing import Dict, List, Tuple

import datasets

from .bigbiohub import (BigBioConfig, Tasks, brat_parse_to_bigbio_kb,
                        kb_features, parse_brat_file)

_LANGUAGES = ["English"]
_PUBMED = False
_LOCAL = False
_CITATION = """\
@article{10.1093/bioadv/vbae116,
    author = {Nastou, Katerina and Koutrouli, Mikaela and Pyysalo, Sampo and Jensen, Lars Juhl},
    title = "{CoNECo: A Corpus for Named Entity Recognition and Normalization of Protein Complexes}",
    journal = {Bioinformatics Advances},
    pages = {vbae116},
    year = {2024},
    month = {08},
    abstract = "{Despite significant progress in biomedical information extraction, there is a lack of resources \
for Named Entity Recognition (NER) and Normalization (NEN) of protein-containing complexes. Current resources \
inadequately address the recognition of protein-containing complex names across different organisms, underscoring \
the crucial need for a dedicated corpus.We introduce the Complex Named Entity Corpus (CoNECo), an annotated \
corpus for NER and NEN of complexes. CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are \
normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a \
transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, \
with F-scores of 73.7\\% and 61.2\\%, respectively. Subsequently, we applied the best taggers for comprehensive \
tagging of the entire openly accessible biomedical literature.All resources, including the annotated corpus, \
training data, and code, are available to the community through Zenodo https://zenodo.org/records/11263147 and \
GitHub https://zenodo.org/records/10693653.}",
    issn = {2635-0041},
    doi = {10.1093/bioadv/vbae116},
    url = {https://doi.org/10.1093/bioadv/vbae116},
    eprint = {https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbae116/\
58869902/vbae116.pdf},
}
"""

_DATASETNAME = "coneco"
_DISPLAYNAME = "CoNECo"

_DESCRIPTION = """\
Complex Named Entity Corpus (CoNECo) is an annotated corpus for NER and NEN of protein-containing complexes. \
CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are normalized to Gene Ontology. We \
divided the corpus into training, development, and test sets.
"""

_HOMEPAGE = "https://zenodo.org/records/11263147"

_LICENSE = "CC_BY_4p0"

_URLS = {
    _DATASETNAME: "https://zenodo.org/records/11263147/files/CoNECo_corpus.tar.gz?download=1",
}

_SUPPORTED_TASKS = [
    Tasks.NAMED_ENTITY_RECOGNITION,
    Tasks.NAMED_ENTITY_DISAMBIGUATION,
]

_SOURCE_VERSION = "2.0.0"

_BIGBIO_VERSION = "1.0.0"


class ConecoDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="coneco_source",
            version=SOURCE_VERSION,
            description="coneco source schema",
            schema="source",
            subset_id="coneco",
        ),
        BigBioConfig(
            name="coneco_bigbio_kb",
            version=BIGBIO_VERSION,
            description="coneco BigBio schema",
            schema="bigbio_kb",
            subset_id="coneco",
        ),
    ]

    DEFAULT_CONFIG_NAME = "coneco_source"

    def _info(self) -> datasets.DatasetInfo:
        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, i.e. entities for NER task
                        {
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "type": datasets.Value("string"),
                            "id": datasets.Value("string"),
                        }
                    ],
                    "normalizations": [  # N lines in brat, i.e. normalization for NEN task
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "ref_id": datasets.Value("string"),
                            "resource_name": datasets.Value("string"),
                            "cuid": datasets.Value("string"),
                            "text": 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))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # Whatever you put in gen_kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir / "train",
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": data_dir / "test",
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_dir / "dev",
                    "split": "dev",
                },
            ),
        ]

    def _filter_oos_entities(self, brat_parse):
        """Filter out entity annotations with out-of-scope type."""
        brat_parse["text_bound_annotations"] = [a for a in brat_parse["text_bound_annotations"] if a["type"] != "OOS"]
        return brat_parse

    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        if self.config.schema == "source":
            for file in sorted(filepath.iterdir()):
                if file.suffix != ".txt":
                    continue
                brat_parsed = parse_brat_file(file)
                brat_parsed = self._filter_oos_entities(brat_parsed)
                brat_parsed["id"] = file.stem

                yield brat_parsed["document_id"], brat_parsed

        elif self.config.schema == "bigbio_kb":
            for file in sorted(filepath.iterdir()):
                if file.suffix != ".txt":
                    continue
                brat_parsed = parse_brat_file(file)
                brat_parsed = self._filter_oos_entities(brat_parsed)
                bigbio_kb_example = brat_parse_to_bigbio_kb(brat_parsed)
                bigbio_kb_example["id"] = file.stem

                yield bigbio_kb_example["id"], bigbio_kb_example