upload hubscripts/bioscope_hub.py to hub from bigbio repo

bioscope.py

@@ -0,0 +1,332 @@
+# 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.
+
+"""
+BioScope
+---
+The corpus consists of three parts, namely medical free texts, biological full
+papers and biological scientific abstracts. The dataset contains annotations at
+the token level for negative and speculative keywords and at the sentence level
+for their linguistic scope. The annotation process was carried out by two
+independent linguist annotators and a chief linguist - also responsible for
+setting up the annotation guidelines - who resolved cases where the annotators
+disagreed. The resulting corpus consists of more than 20.000 sentences that were
+considered for annotation and over 10% of them actually contain one (or more)
+linguistic annotation suggesting negation or uncertainty.
+"""
+
+import os
+import re
+import xml.etree.ElementTree as ET
+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
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{vincze2008bioscope,
+  title={The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes},
+  author={Vincze, Veronika and Szarvas, Gy{\"o}rgy and Farkas, Rich{\'a}rd and M{\'o}ra, Gy{\"o}rgy and Csirik, J{\'a}nos},
+  journal={BMC bioinformatics},
+  volume={9},
+  number={11},
+  pages={1--9},
+  year={2008},
+  publisher={BioMed Central}
+}
+"""
+
+_DATASETNAME = "bioscope"
+_DISPLAYNAME = "BioScope"
+
+
+_DESCRIPTION = """\
+The BioScope corpus consists of medical and biological texts annotated for
+negation, speculation and their linguistic scope. This was done to allow a
+comparison between the development of systems for negation/hedge detection and
+scope resolution. The BioScope corpus was annotated by two independent linguists
+following the guidelines written by our linguist expert before the annotation of
+the corpus was initiated.
+"""
+
+_HOMEPAGE = "https://rgai.inf.u-szeged.hu/node/105"
+
+_LICENSE = 'Creative Commons Attribution 2.0 Generic'
+
+_URLS = {
+    _DATASETNAME: "https://rgai.sed.hu/sites/rgai.sed.hu/files/bioscope.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class BioscopeDataset(datasets.GeneratorBasedBuilder):
+    """The BioScope corpus consists of medical and biological texts annotated for negation, speculation and their linguistic scope."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bioscope_source",
+            version=SOURCE_VERSION,
+            description="bioscope source schema",
+            schema="source",
+            subset_id="bioscope",
+        ),
+        BigBioConfig(
+            name="bioscope_abstracts_source",
+            version=SOURCE_VERSION,
+            description="bioscope source schema",
+            schema="source",
+            subset_id="bioscope_abstracts",
+        ),
+        BigBioConfig(
+            name="bioscope_papers_source",
+            version=SOURCE_VERSION,
+            description="bioscope source schema",
+            schema="source",
+            subset_id="bioscope_papers",
+        ),
+        BigBioConfig(
+            name="bioscope_medical_texts_source",
+            version=SOURCE_VERSION,
+            description="bioscope source schema",
+            schema="source",
+            subset_id="bioscope_medical_texts",
+        ),
+        BigBioConfig(
+            name="bioscope_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="bioscope BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bioscope",
+        ),
+        BigBioConfig(
+            name="bioscope_abstracts_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="bioscope BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bioscope_abstracts",
+        ),
+        BigBioConfig(
+            name="bioscope_papers_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="bioscope BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bioscope_papers",
+        ),
+        BigBioConfig(
+            name="bioscope_medical_texts_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="bioscope BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bioscope_medical_texts",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "bioscope_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "document_type": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                            "normalized": [
+                                {
+                                    "db_name": datasets.Value("string"),
+                                    "db_id": 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_files": data_dir,
+                },
+            )
+        ]
+
+    def _generate_examples(self, data_files: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        sentences = self._load_sentences(data_files)
+        if self.config.schema == "source":
+            for guid, sentence_tuple in enumerate(sentences):
+                document_type, sentence = sentence_tuple
+                example = self._create_example(sentence_tuple)
+                example["document_type"] = f"{document_type}_{sentence.attrib['id']}"
+                example["text"] = "".join(sentence_tuple[1].itertext())
+                yield guid, example
+
+        elif self.config.schema == "bigbio_kb":
+            for guid, sentence_tuple in enumerate(sentences):
+                document_type, sentence = sentence_tuple
+                example = self._create_example(sentence_tuple)
+                example["id"] = guid
+                example["passages"] = [
+                    {
+                        "id": f"{document_type}_{sentence.attrib['id']}",
+                        "type": document_type,
+                        "text": ["".join(sentence.itertext())],
+                        "offsets": [(0, len("".join(sentence.itertext())))],
+                    }
+                ]
+                example["events"] = []
+                example["coreferences"] = []
+                example["relations"] = []
+                yield guid, example
+
+    def _load_sentences(self, data_files: Path) -> List:
+        """
+        Returns a list of tuples (Document type, iterator from dataset)
+        """
+        if self.config.subset_id.__contains__("abstracts"):
+            sentences = self._concat_iterators(
+                (
+                    "Abstract",
+                    ET.parse(os.path.join(data_files, "abstracts.xml"))
+                    .getroot()
+                    .iter("sentence"),
+                )
+            )
+        elif self.config.subset_id.__contains__("papers"):
+            sentences = self._concat_iterators(
+                (
+                    "Paper",
+                    ET.parse(os.path.join(data_files, "full_papers.xml"))
+                    .getroot()
+                    .iter("sentence"),
+                )
+            )
+        elif self.config.subset_id.__contains__("medical_texts"):
+            sentences = self._concat_iterators(
+                (
+                    "Medical text",
+                    ET.parse(
+                        os.path.join(
+                            data_files, "clinical_merger/clinical_records_anon.xml"
+                        )
+                    )
+                    .getroot()
+                    .iter("sentence"),
+                )
+            )
+        else:
+            abstracts = (
+                ET.parse(os.path.join(data_files, "abstracts.xml"))
+                .getroot()
+                .iter("sentence")
+            )
+            papers = (
+                ET.parse(os.path.join(data_files, "full_papers.xml"))
+                .getroot()
+                .iter("sentence")
+            )
+            medical_texts = (
+                ET.parse(
+                    os.path.join(
+                        data_files, "clinical_merger/clinical_records_anon.xml"
+                    )
+                )
+                .getroot()
+                .iter("sentence")
+            )
+            sentences = self._concat_iterators(
+                ("Abstract", abstracts),
+                ("Paper", papers),
+                ("Medical text", medical_texts),
+            )
+        return sentences
+
+    @staticmethod
+    def _concat_iterators(*iterator_tuple):
+        for document_type, iterator in iterator_tuple:
+            for element in iterator:
+                yield document_type, element
+
+    def _create_example(self, sentence_tuple):
+        document_type, sentence = sentence_tuple
+        document_type_prefix = document_type[0]
+
+        example = {}
+        example["document_id"] = f"{document_type_prefix}_{sentence.attrib['id']}"
+        example["entities"] = self._extract_entities(sentence, document_type_prefix)
+        return example
+
+    def _extract_entities(self, sentence, document_type_prefix):
+        text = "".join(sentence.itertext())
+        entities = []
+        xcopes = dict([(xcope.attrib["id"], xcope) for xcope in sentence.iter("xcope")])
+        cues = dict([(cue.attrib["ref"], cue) for cue in sentence.iter("cue")])
+        for idx, xcope in xcopes.items():
+            # X2.140.2 has no annotation in raw data
+            if cues.get(idx) is None:
+                continue
+            entities.append(
+                {
+                    "id": f"{document_type_prefix}_{idx}",
+                    "type": cues.get(idx).attrib["type"],
+                    "text": ["".join(xcope.itertext())],
+                    "offsets": self._extract_offsets(
+                        text=text, entity_text="".join(xcope.itertext())
+                    ),
+                    "normalized": [],
+                }
+            )
+        return entities
+
+    def _extract_offsets(self, text, entity_text):
+        return [(text.find(entity_text), text.find(entity_text) + len(entity_text))]