genia_ptm_event_corpus.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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"""
Post-translational-modifications (PTM), amino acid modifications of proteins after translation, are one of the posterior
processes of protein biosynthesis for many proteins, and they are critical for determining protein function such as its
activity state, localization, turnover and interactions with other biomolecules. While there have been many studies of
information extraction targeting individual PTM types, there was until recently little effort to address extraction of
multiple PTM types at once in a unified framework.
"""

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

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


_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{ohta-etal-2010-event,
    title = "Event Extraction for Post-Translational Modifications",
    author = "Ohta, Tomoko  and
      Pyysalo, Sampo  and
      Miwa, Makoto  and
      Kim, Jin-Dong  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2010 Workshop on Biomedical Natural Language Processing",
    month = jul,
    year = "2010",
    address = "Uppsala, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W10-1903",
    pages = "19--27",
}
"""

_DATASETNAME = "genia_ptm_event_corpus"
_DISPLAYNAME = "PTM Events"

_DESCRIPTION = """\
Post-translational-modifications (PTM), amino acid modifications of proteins \
after translation, are one of the posterior processes of protein biosynthesis \
for many proteins, and they are critical for determining protein function such \
as its activity state, localization, turnover and interactions with other \
biomolecules. While there have been many studies of information extraction \
targeting individual PTM types, there was until recently little effort to \
address extraction of multiple PTM types at once in a unified framework.
"""

_HOMEPAGE = "http://www.geniaproject.org/other-corpora/ptm-event-corpus"

_LICENSE = 'GENIA Project License for Annotated Corpora'


_URLS = {
    _DATASETNAME: "http://www.geniaproject.org/other-corpora/ptm-event-corpus/post-translational_modifications_training_data.tar.gz?attredirects=0&d=1",
}

_SUPPORTED_TASKS = [
    Tasks.NAMED_ENTITY_RECOGNITION,
    Tasks.COREFERENCE_RESOLUTION,
    Tasks.EVENT_EXTRACTION,
]

_SOURCE_VERSION = "1.0.0"

_BIGBIO_VERSION = "1.0.0"


class GeniaPtmEventCorpusDataset(datasets.GeneratorBasedBuilder):
    """GENIA PTM event corpus."""

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="genia_ptm_event_corpus_source",
            version=SOURCE_VERSION,
            description="genia_ptm_event_corpus source schema",
            schema="source",
            subset_id="genia_ptm_event_corpus",
        ),
        BigBioConfig(
            name="genia_ptm_event_corpus_bigbio_kb",
            version=BIGBIO_VERSION,
            description="genia_ptm_event_corpus BigBio schema",
            schema="bigbio_kb",
            subset_id="genia_ptm_event_corpus",
        ),
    ]

    DEFAULT_CONFIG_NAME = "genia_ptm_event_corpus_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, 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
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value(
                                "string"
                            ),  # refers to the text_bound_annotation of the trigger
                            "trigger": datasets.Value("string"),
                            "arguments": [
                                {
                                    "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")),
                        }
                    ],
                },
            )

        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_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_dir": data_dir,
                },
            ),
        ]

    def _generate_examples(self, data_dir) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        for dirpath, _, filenames in os.walk(data_dir):
            for guid, filename in enumerate(filenames):
                if filename.endswith(".txt"):
                    txt_file_path = Path(dirpath, filename)
                    if self.config.schema == "source":
                        example = parse_brat_file(
                            txt_file_path, annotation_file_suffixes=[".a1", ".a2"]
                        )
                        example["id"] = str(guid)
                        for key in ["attributes", "normalizations"]:
                            del example[key]
                        yield guid, example
                    elif self.config.schema == "bigbio_kb":
                        example = brat_parse_to_bigbio_kb(
                            parse_brat_file(txt_file_path)
                        )
                        example["id"] = str(guid)
                        yield guid, example