bionlp_st_2013_ge.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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from pathlib import Path
from typing import 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_2013_ge"
_DISPLAYNAME = "BioNLP 2013 GE"

_SOURCE_VIEW_NAME = "source"
_UNIFIED_VIEW_NAME = "bigbio"

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{kim-etal-2013-genia,
    title = "The {G}enia Event Extraction Shared Task, 2013 Edition - Overview",
    author = "Kim, Jin-Dong  and
      Wang, Yue  and
      Yasunori, Yamamoto",
    booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
    month = aug,
    year = "2013",
    address = "Sofia, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W13-2002",
    pages = "8--15",
}
"""

_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
"""

_HOMEPAGE = "https://github.com/openbiocorpora/bionlp-st-2013-ge"

_LICENSE = 'GENIA Project License for Annotated Corpora'

_URLs = {
    "source": "https://github.com/openbiocorpora/bionlp-st-2013-ge/archive/refs/heads/master.zip",
    "bigbio_kb": "https://github.com/openbiocorpora/bionlp-st-2013-ge/archive/refs/heads/master.zip",
}

_SUPPORTED_TASKS = [
    Tasks.EVENT_EXTRACTION,
    Tasks.NAMED_ENTITY_RECOGNITION,
    Tasks.RELATION_EXTRACTION,
    Tasks.COREFERENCE_RESOLUTION,
]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class bionlp_st_2013_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_2013_ge_source",
            version=SOURCE_VERSION,
            description="bionlp_st_2013_ge source schema",
            schema="source",
            subset_id="bionlp_st_2013_ge",
        ),
        BigBioConfig(
            name="bionlp_st_2013_ge_bigbio_kb",
            version=BIGBIO_VERSION,
            description="bionlp_st_2013_ge BigBio schema",
            schema="bigbio_kb",
            subset_id="bionlp_st_2013_ge",
        ),
    ]

    DEFAULT_CONFIG_NAME = "bionlp_st_2013_ge_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"),
                            "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]:

        my_urls = _URLs[self.config.schema]
        data_dir = Path(dl_manager.download_and_extract(my_urls))
        data_files = {
            "train": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "train",
            "dev": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "devel",
            "test": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "test",
        }

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_files": data_files["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"data_files": data_files["dev"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"data_files": data_files["test"]},
            ),
        ]

    def _generate_examples(self, data_files: Path):
        if self.config.schema == "source":
            txt_files = list(data_files.glob("*txt"))
            for guid, txt_file in enumerate(txt_files):
                example = parse_brat_file(txt_file)
                example["id"] = str(guid)
                yield guid, example
        elif self.config.schema == "bigbio_kb":
            txt_files = list(data_files.glob("*txt"))
            for guid, txt_file in enumerate(txt_files):
                example = brat_parse_to_bigbio_kb(
                    parse_brat_file(txt_file)
                )
                example["id"] = str(guid)
                yield guid, example
        else:
            raise ValueError(f"Invalid config: {self.config.name}")