bioasq_2021_mesinesp.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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"""
The main aim of MESINESP2 is to promote the development of practically relevant
semantic indexing tools for biomedical content in non-English language. We have
generated a manually annotated corpus, where domain experts have labeled a set
of scientific literature, clinical trials, and patent abstracts. All the
documents were labeled with DeCS descriptors, which is a structured controlled
vocabulary created by BIREME to index scientific publications on BvSalud, the
largest database of scientific documents in Spanish, which hosts records from
the databases LILACS, MEDLINE, IBECS, among others.

MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS
to different types of biomedical documents. To that purpose, we have divided the
task into three subtracks depending on the document type. Then, for each one we
generated an annotated corpus which was provided to participating teams:

- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all
  Spanish records from LILACS and IBECS databases at the Virtual Health Library
  (VHL) with non-empty abstract written in Spanish.
- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro
  Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the
  structure title/abstract needed in BioASQ, for that reason we have built
  artificial abstracts based on the content available in the data crawled using
  the REEC API.
- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in
  Spanish extracted from Google Patents which have the IPC code “A61P” and
  “A61K31”. In addition, we also provide a set of complementary data such as:
  the DeCS terminology file, a silver standard with the participants' predictions
  to the task background set and the entities of medications, diseases, symptoms
  and medical procedures extracted from the BSC NERs documents.
"""

import json
import os
from typing import Dict, List, Tuple

import datasets

from .bigbiohub import text_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['Spanish']
_PUBMED = False
_LOCAL = False
_CITATION = """\
@conference {396,
    title = {Overview of BioASQ 2021-MESINESP track. Evaluation of
    advance hierarchical classification techniques for scientific
    literature, patents and clinical trials.},
    booktitle = {Proceedings of the 9th BioASQ Workshop
    A challenge on large-scale biomedical semantic indexing
    and question answering},
    year = {2021},
    url = {http://ceur-ws.org/Vol-2936/paper-11.pdf},
    author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia
     and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a,
     Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin}
}

"""

_DATASETNAME = "bioasq_2021_mesinesp"
_DISPLAYNAME = "MESINESP 2021"

_DESCRIPTION = """\
The main aim of MESINESP2 is to promote the development of practically relevant \
semantic indexing tools for biomedical content in non-English language. We have \
generated a manually annotated corpus, where domain experts have labeled a set \
of scientific literature, clinical trials, and patent abstracts. All the \
documents were labeled with DeCS descriptors, which is a structured controlled \
vocabulary created by BIREME to index scientific publications on BvSalud, the \
largest database of scientific documents in Spanish, which hosts records from \
the databases LILACS, MEDLINE, IBECS, among others.

MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS \
to different types of biomedical documents. To that purpose, we have divided the \
task into three subtracks depending on the document type. Then, for each one we \
generated an annotated corpus which was provided to participating teams:

- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all \
  Spanish records from LILACS and IBECS databases at the Virtual Health Library \
  (VHL) with non-empty abstract written in Spanish.
- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro \
  Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the \
  structure title/abstract needed in BioASQ, for that reason we have built \
  artificial abstracts based on the content available in the data crawled using \
  the REEC API.
- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in \
  Spanish extracted from Google Patents which have the IPC code “A61P” and \
  “A61K31”. In addition, we also provide a set of complementary data such as: \
  the DeCS terminology file, a silver standard with the participants' predictions \
  to the task background set and the entities of medications, diseases, symptoms \
  and medical procedures extracted from the BSC NERs documents.
"""

_HOMEPAGE = "https://zenodo.org/record/5602914#.YhSXJ5PMKWt"

_LICENSE = 'Creative Commons Attribution 4.0 International'

_URLS = {
    _DATASETNAME: {
        "subtrack1": "https://zenodo.org/record/5602914/files/Subtrack1-Scientific_Literature.zip?download=1",
        "subtrack2": "https://zenodo.org/record/5602914/files/Subtrack2-Clinical_Trials.zip?download=1",
        "subtrack3": "https://zenodo.org/record/5602914/files/Subtrack3-Patents.zip?download=1",
    },
}

_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]

_SOURCE_VERSION = "1.0.6"
_BIGBIO_VERSION = "1.0.0"


class Bioasq2021MesinespDataset(datasets.GeneratorBasedBuilder):
    """\
    A dataset to promote the development of practically relevant
    semantic indexing tools for biomedical content in non-English language.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack1_all_source",
            version=SOURCE_VERSION,
            description="bioasq_2021_mesinesp source schema subtrack1",
            schema="source",
            subset_id="bioasq_2021_mesinesp_subtrack1_all",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack1_only_articles_source",
            version=SOURCE_VERSION,
            description="bioasq_2021_mesinesp source schema subtrack1",
            schema="source",
            subset_id="bioasq_2021_mesinesp_subtrack1_only_articles",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack2_source",
            version=SOURCE_VERSION,
            description="bioasq_2021_mesinesp source schema subtrack2",
            schema="source",
            subset_id="bioasq_2021_mesinesp_subtrack2",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack3_source",
            version=SOURCE_VERSION,
            description="bioasq_2021_mesinesp source schema subtrack3",
            schema="source",
            subset_id="bioasq_2021_mesinesp_subtrack3",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack1_all_bigbio_text",
            version=BIGBIO_VERSION,
            description="bioasq_2021_mesinesp BigBio schema subtrack1",
            schema="bigbio_text",
            subset_id="bioasq_2021_mesinesp_subtrack1_all",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack1_only_articles_bigbio_text",
            version=BIGBIO_VERSION,
            description="bioasq_2021_mesinesp BigBio schema subtrack1",
            schema="bigbio_text",
            subset_id="bioasq_2021_mesinesp_subtrack1_only_articles",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack2_bigbio_text",
            version=BIGBIO_VERSION,
            description="bioasq_2021_mesinesp BigBio schema subtrack2",
            schema="bigbio_text",
            subset_id="bioasq_2021_mesinesp_subtrack2",
        ),
        BigBioConfig(
            name="bioasq_2021_mesinesp_subtrack3_bigbio_text",
            version=BIGBIO_VERSION,
            description="bioasq_2021_mesinesp BigBio schema subtrack3",
            schema="bigbio_text",
            subset_id="bioasq_2021_mesinesp_subtrack3",
        ),
    ]

    DEFAULT_CONFIG_NAME = "bioasq_2021_mesinesp_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "abstractText": datasets.Value("string"),
                    "db": datasets.Value("string"),
                    "decsCodes": datasets.Sequence(datasets.Value("string")),
                    "id": datasets.Value("string"),
                    "journal": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "year": datasets.Value("string"),
                }
            )
        elif self.config.schema == "bigbio_text":
            features = text_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."""

        if "subtrack1" in self.config.name:
            track = "1"
        elif "subtrack2" in self.config.name:
            track = "2"
        else:
            track = "3"

        urls = _URLS[_DATASETNAME][f"subtrack{track}"]
        if self.config.data_dir is None:
            try:
                data_dir = dl_manager.download_and_extract(urls)
            except ConnectionError:
                raise ConnectionError(
                    "Could not download. Save locally and use `data_dir` kwarg"
                )
        else:
            data_dir = self.config.data_dir

        if track == "1":
            top_folder = "Subtrack1-Scientific_Literature"
        elif track == "2":
            top_folder = "Subtrack2-Clinical_Trials"
        else:
            top_folder = "Subtrack3-Patents"
        if track == "1":
            if "all" in self.config.name:
                train_filepath = "training_set_subtrack1_all.json"
            else:
                train_filepath = "training_set_subtrack1_only_articles.json"
        else:
            train_filepath = f"training_set_subtrack{track}.json"

        dev_filepath = f"development_set_subtrack{track}.json"
        test_filepath = f"test_set_subtrack{track}.json"

        split_gens = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, top_folder, "Train", train_filepath
                    ),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, top_folder, "Development", dev_filepath
                    ),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, top_folder, "Test", test_filepath
                    ),
                },
            ),
        ]

        # track 3 doesn't have Train data
        if track == "3":
            return split_gens[1:]

        return split_gens

    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""

        if self.config.schema == "source":

            with open(filepath) as fp:
                data = json.load(fp)

            for key, example in enumerate(data["articles"]):
                yield key, example

        elif self.config.schema == "bigbio_text":
            with open(filepath) as fp:
                data = json.load(fp)

            for key, example in enumerate(data["articles"]):
                yield key, {
                    "id": example["id"],
                    "document_id": "NULL",
                    "text": example["abstractText"],
                    "labels": example["decsCodes"],
                }