jnlpba.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.
# 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 data came from the GENIA version 3.02 corpus (Kim et al., 2003).
This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors.
From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on
a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
"""

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

# TODO: Add BibTeX citation
_CITATION = """\
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop
on Natural Language Processing in Biomedicine and its Applications
({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th", year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
"""

_DATASETNAME = "jnlpba"
_DISPLAYNAME = "JNLPBA"

_DESCRIPTION = """\
NER For Bio-Entities
"""

_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"

_LICENSE = 'Creative Commons Attribution 3.0 Unported'

_URLS = {
    _DATASETNAME: "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz",
}

# TODO: add supported task by dataset. One dataset may support multiple tasks
_SUPPORTED_TASKS = [
    Tasks.NAMED_ENTITY_RECOGNITION
]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]

# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0"
#  This version doesn't have to be consistent with semantic versioning. Anything that is
#  provided by the original dataset as a version goes.
_SOURCE_VERSION = "3.2.0"

_BIGBIO_VERSION = "1.0.0"


class JNLPBADataset(datasets.GeneratorBasedBuilder):
    """
    The data came from the GENIA version 3.02 corpus
    (Kim et al., 2003).
    This was formed from a controlled search on MEDLINE
    using the MeSH terms human, blood cells and transcription factors.
    From this search 2,000 abstracts were selected and hand annotated
    according to a small taxonomy of 48 classes based on
    a chemical classification.
    Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="jnlpba_source",
            version=SOURCE_VERSION,
            description="jnlpba source schema",
            schema="source",
            subset_id="jnlpba",
        ),
        BigBioConfig(
            name="jnlpba_bigbio_kb",
            version=BIGBIO_VERSION,
            description="jnlpba BigBio schema",
            schema="bigbio_kb",
            subset_id="jnlpba",
        ),
    ]

    DEFAULT_CONFIG_NAME = "jnlpba_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.load_dataset("jnlpba", split="train").features

        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."""
        data = datasets.load_dataset("jnlpba")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # Whatever you put in gen_kwargs will be passed to _generate_examples
                gen_kwargs={"data": data["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"data": data["validation"]},
            ),
        ]

    def _generate_examples(self, data: datasets.Dataset) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        uid = 0

        if self.config.schema == "source":
            for key, sample in enumerate(data):
                yield key, sample

        elif self.config.schema == "bigbio_kb":
            for i, sample in enumerate(data):
                feature_dict = {
                    "id": uid,
                    "document_id": "NULL",
                    "passages": [],
                    "entities": [],
                    "relations": [],
                    "events": [],
                    "coreferences": [],
                }

                uid += 1
                offset_start = 0
                for token, tag in zip(sample["tokens"], sample["ner_tags"]):
                    offset_start += len(token) + 1
                    feature_dict["entities"].append(
                        {
                            "id": uid,
                            "offsets": [[offset_start, offset_start + len(token)]],
                            "text": [token],
                            "type": tag,
                            "normalized": [],
                        }
                    )
                    uid += 1

                # entities
                yield i, feature_dict