bc5cdr.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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"""
To this end, we set up a challenge task through BioCreative V to automatically
extract CDRs from the literature. More specifically, we designed two challenge
tasks: disease named entity recognition (DNER) and chemical-induced disease
(CID) relation extraction. To assist system development and assessment, we
created a large annotated text corpus that consists of human annotations of
all chemicals, diseases and their interactions in 1,500 PubMed articles.

-- 'Overview of the BioCreative V Chemical Disease Relation (CDR) Task'
"""
import collections
import itertools
import os

import datasets
from bioc import biocxml

from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
from .bigbiohub import get_texts_and_offsets_from_bioc_ann


_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{DBLP:journals/biodb/LiSJSWLDMWL16,
  author    = {Jiao Li and
               Yueping Sun and
               Robin J. Johnson and
               Daniela Sciaky and
               Chih{-}Hsuan Wei and
               Robert Leaman and
               Allan Peter Davis and
               Carolyn J. Mattingly and
               Thomas C. Wiegers and
               Zhiyong Lu},
  title     = {BioCreative {V} {CDR} task corpus: a resource for chemical disease
               relation extraction},
  journal   = {Database J. Biol. Databases Curation},
  volume    = {2016},
  year      = {2016},
  url       = {https://doi.org/10.1093/database/baw068},
  doi       = {10.1093/database/baw068},
  timestamp = {Thu, 13 Aug 2020 12:41:41 +0200},
  biburl    = {https://dblp.org/rec/journals/biodb/LiSJSWLDMWL16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""

_DATASETNAME = "bc5cdr"
_DISPLAYNAME = "BC5CDR"

_DESCRIPTION = """\
The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated \
text corpus of human annotations of all chemicals, diseases and their \
interactions in 1,500 PubMed articles.
"""

_HOMEPAGE = "http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/"

_LICENSE = 'Public Domain Mark 1.0'

_URLs = {
    "source": "https://huggingface.co/datasets/bigbio/bc5cdr/resolve/main/CDR_Data.zip",
    "bigbio_kb": "https://huggingface.co/datasets/bigbio/bc5cdr/resolve/main/CDR_Data.zip",
}

_SUPPORTED_TASKS = [
    Tasks.NAMED_ENTITY_RECOGNITION,
    Tasks.NAMED_ENTITY_DISAMBIGUATION,
    Tasks.RELATION_EXTRACTION,
]
_SOURCE_VERSION = "01.05.16"
_BIGBIO_VERSION = "1.0.0"


class Bc5cdrDataset(datasets.GeneratorBasedBuilder):
    """
    BioCreative V Chemical Disease Relation (CDR) Task.
    """

    DEFAULT_CONFIG_NAME = "bc5cdr_source"
    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="bc5cdr_source",
            version=SOURCE_VERSION,
            description="BC5CDR source schema",
            schema="source",
            subset_id="bc5cdr",
        ),
        BigBioConfig(
            name="bc5cdr_bigbio_kb",
            version=BIGBIO_VERSION,
            description="BC5CDR simplified BigBio schema",
            schema="bigbio_kb",
            subset_id="bc5cdr",
        ),
    ]

    def _info(self):

        if self.config.schema == "source":
            # this is a variation on the BioC format
            features = datasets.Features(
                {
                    "passages": [
                        {
                            "document_id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "text": datasets.Value("string"),
                            "entities": [
                                {
                                    "id": datasets.Value("string"),
                                    "offsets": [[datasets.Value("int32")]],
                                    "text": [datasets.Value("string")],
                                    "type": datasets.Value("string"),
                                    "normalized": [
                                        {
                                            "db_name": datasets.Value("string"),
                                            "db_id": datasets.Value("string"),
                                        }
                                    ],
                                }
                            ],
                            "relations": [
                                {
                                    "id": datasets.Value("string"),
                                    "type": datasets.Value("string"),
                                    "arg1_id": datasets.Value("string"),
                                    "arg2_id": datasets.Value("string"),
                                }
                            ],
                        }
                    ]
                }
            )

        elif self.config.schema == "bigbio_kb":
            features = kb_features

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=str(_LICENSE),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        my_urls = _URLs[self.config.schema]
        data_dir = dl_manager.download_and_extract(my_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_TrainingSet.BioC.xml"
                    ),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_TestSet.BioC.xml"
                    ),
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir,
                        "CDR_Data/CDR.Corpus.v010516/CDR_DevelopmentSet.BioC.xml",
                    ),
                    "split": "dev",
                },
            ),
        ]

    def _get_bioc_entity(self, span, doc_text, db_id_key="MESH"):
        """Parse BioC entity annotation.

        Parameters
        ----------
        span : BioCAnnotation
            BioC entity annotation
        doc_text : string
            document text, required to construct text spans
        db_id_key : str, optional
            database name used for normalization, by default "MESH"

        Returns
        -------
        dict
            entity information
        """
        # offsets = [(loc.offset, loc.offset + loc.length) for loc in span.locations]
        # texts = [doc_text[i:j] for i, j in offsets]
        offsets, texts = get_texts_and_offsets_from_bioc_ann(span)
        db_ids = span.infons[db_id_key] if db_id_key else "-1"

        # some entities are not linked and
        # some entities are linked to multiple normalized ids
        if db_ids == "-1":
            db_ids_list = []
        else:
            db_ids_list = db_ids.split("|")

        normalized = [{"db_name": db_id_key, "db_id": db_id} for db_id in db_ids_list]

        return {
            "id": span.id,
            "offsets": offsets,
            "text": texts,
            "type": span.infons["type"],
            "normalized": normalized,
        }

    def _get_relations(self, relations, entities):
        """
        BC5CDR provides abstract-level annotations for entity-linked relation
        pairs rather than materializing links between all surface form
        mentions of relations. An example from train id=2670794, the relation
            - (chemical, disease) (D014148, D004211)
        is materialized as 6 mentions of entity pairs
            - 2x ('tranexamic acid', 'intravascular coagulation')
            - 4x ('AMCA', 'intravascular coagulation')
        """
        # index entities by normalized id
        index = collections.defaultdict(list)
        for ent in entities:
            for norm in ent["normalized"]:
                index[norm["db_id"]].append(ent)
        index = dict(index)

        # transform doc-level relations to mention-level
        rela_mentions = []
        for rela in relations:
            arg1 = rela.infons["Chemical"]
            arg2 = rela.infons["Disease"]
            # all mention pairs
            all_pairs = itertools.product(index[arg1], index[arg2])
            for a, b in all_pairs:
                # create relations linked by entity ids
                rela_mentions.append(
                    {
                        "id": None,
                        "type": rela.infons["relation"],
                        "arg1_id": a["id"],
                        "arg2_id": b["id"],
                        "normalized": [],
                    }
                )
        return rela_mentions

    def _get_document_text(self, xdoc):
        """Build document text for unit testing entity span offsets."""
        text = ""
        for passage in xdoc.passages:
            pad = passage.offset - len(text)
            text += (" " * pad) + passage.text
        return text

    def _generate_examples(
        self,
        filepath,
        split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """Yields examples as (key, example) tuples."""
        if self.config.schema == "source":
            reader = biocxml.BioCXMLDocumentReader(str(filepath))

            for uid, xdoc in enumerate(reader):
                doc_text = self._get_document_text(xdoc)
                yield uid, {
                    "passages": [
                        {
                            "document_id": xdoc.id,
                            "type": passage.infons["type"],
                            "text": passage.text,
                            "entities": [
                                self._get_bioc_entity(span, doc_text)
                                for span in passage.annotations
                            ],
                            "relations": [
                                {
                                    "id": rel.id,
                                    "type": rel.infons["relation"],
                                    "arg1_id": rel.infons["Chemical"],
                                    "arg2_id": rel.infons["Disease"],
                                }
                                for rel in xdoc.relations
                            ],
                        }
                        for passage in xdoc.passages
                    ]
                }

        elif self.config.schema == "bigbio_kb":
            reader = biocxml.BioCXMLDocumentReader(str(filepath))
            uid = 0  # global unique id

            for i, xdoc in enumerate(reader):
                data = {
                    "id": uid,
                    "document_id": xdoc.id,
                    "passages": [],
                    "entities": [],
                    "relations": [],
                    "events": [],
                    "coreferences": [],
                }
                uid += 1
                doc_text = self._get_document_text(xdoc)

                char_start = 0
                # passages must not overlap and spans must cover the entire document
                for passage in xdoc.passages:
                    offsets = [[char_start, char_start + len(passage.text)]]
                    char_start = char_start + len(passage.text) + 1
                    data["passages"].append(
                        {
                            "id": uid,
                            "type": passage.infons["type"],
                            "text": [passage.text],
                            "offsets": offsets,
                        }
                    )
                    uid += 1

                # entities
                for passage in xdoc.passages:
                    for span in passage.annotations:
                        ent = self._get_bioc_entity(span, doc_text, db_id_key="MESH")
                        ent["id"] = uid  # override BioC default id
                        data["entities"].append(ent)
                        uid += 1

                # relations
                relations = self._get_relations(xdoc.relations, data["entities"])
                for rela in relations:
                    rela["id"] = uid  # assign unique id
                    data["relations"].append(rela)
                    uid += 1

                yield i, data