upload hubscripts/genia_term_corpus_hub.py to hub from bigbio repo

genia_term_corpus.py

@@ -0,0 +1,313 @@
+# 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 identification of linguistic expressions referring to entities of interest in molecular biology such as proteins,
+genes and cells is a fundamental task in biomolecular text mining. The GENIA technical term annotation covers the
+identification of  physical biological entities as well as other important terms. The corpus annotation covers the full
+1,999 abstracts of the primary GENIA corpus.
+"""
+
+import xml.etree.ElementTree as ET
+from itertools import count
+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 = """\
+@inproceedings{10.5555/1289189.1289260,
+author = {Ohta, Tomoko and Tateisi, Yuka and Kim, Jin-Dong},
+title = {The GENIA Corpus: An Annotated Research Abstract Corpus in Molecular Biology Domain},
+year = {2002},
+publisher = {Morgan Kaufmann Publishers Inc.},
+address = {San Francisco, CA, USA},
+booktitle = {Proceedings of the Second International Conference on Human Language Technology Research},
+pages = {82–86},
+numpages = {5},
+location = {San Diego, California},
+series = {HLT '02}
+}
+
+@article{Kim2003GENIAC,
+  title={GENIA corpus - a semantically annotated corpus for bio-textmining},
+  author={Jin-Dong Kim and Tomoko Ohta and Yuka Tateisi and Junichi Tsujii},
+  journal={Bioinformatics},
+  year={2003},
+  volume={19 Suppl 1},
+  pages={
+          i180-2
+        }
+}
+
+@inproceedings{10.5555/1567594.1567610,
+author = {Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
+title = {Introduction to the Bio-Entity Recognition Task at JNLPBA},
+year = {2004},
+publisher = {Association for Computational Linguistics},
+address = {USA},
+booktitle = {Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its
+Applications},
+pages = {70–75},
+numpages = {6},
+location = {Geneva, Switzerland},
+series = {JNLPBA '04}
+}
+"""
+
+_DATASETNAME = "genia_term_corpus"
+_DISPLAYNAME = "GENIA Term Corpus"
+
+_DESCRIPTION = """\
+The identification of linguistic expressions referring to entities of interest in molecular biology such as proteins,
+genes and cells is a fundamental task in biomolecular text mining. The GENIA technical term annotation covers the
+identification of  physical biological entities as well as other important terms. The corpus annotation covers the full
+1,999 abstracts of the primary GENIA corpus.
+"""
+
+_HOMEPAGE = "http://www.geniaproject.org/genia-corpus/term-corpus"
+
+_LICENSE = 'GENIA Project License for Annotated Corpora'
+
+_URLS = {
+    _DATASETNAME: "http://www.nactem.ac.uk/GENIA/current/GENIA-corpus/Term/GENIAcorpus3.02.tgz",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "3.0.2"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class GeniaTermCorpusDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="genia_term_corpus_source",
+            version=SOURCE_VERSION,
+            description="genia_term_corpus source schema",
+            schema="source",
+            subset_id="genia_term_corpus",
+        ),
+        BigBioConfig(
+            name="genia_term_corpus_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="genia_term_corpus BigBio schema",
+            schema="bigbio_kb",
+            subset_id="genia_term_corpus",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "genia_term_corpus_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "title": [
+                        {
+                            "text": datasets.Value("string"),
+                            "entities": [
+                                {
+                                    "text": datasets.Value("string"),
+                                    "lex": datasets.Value("string"),
+                                    "sem": datasets.Value("string"),
+                                }
+                            ],
+                        }
+                    ],
+                    "abstract": [
+                        {
+                            "text": datasets.Value("string"),
+                            "entities": [
+                                {
+                                    "text": datasets.Value("string"),
+                                    "lex": datasets.Value("string"),
+                                    "sem": 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(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archive": dl_manager.iter_archive(data_dir),
+                    "data_path": "GENIA_term_3.02/GENIAcorpus3.02.xml",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, archive, data_path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        uid = count(0)
+        for path, file in archive:
+            if path == data_path:
+                for key, example in enumerate(iterparse_genia(file)):
+                    if self.config.schema == "source":
+                        yield key, example
+
+                    elif self.config.schema == "bigbio_kb":
+                        yield key, parse_genia_to_bigbio(example, uid)
+
+
+def iterparse_genia(file):
+    # ontology = None
+    for _, element in ET.iterparse(file):
+        # if element.tag == "import":
+        #     ontology = {"name": element.get("resource"), "prefix": element.get("prefix")}
+        if element.tag == "article":
+            bibliomisc = element.find("articleinfo/bibliomisc").text
+            document_id = parse_genia_bibliomisc(bibliomisc)
+            title = element.find("title")
+            title_sentences = parse_genia_sentences(title)
+            abstract = element.find("abstract")
+            abstract_sentences = parse_genia_sentences(abstract)
+            yield {
+                "document_id": document_id,
+                "title": title_sentences,
+                "abstract": abstract_sentences,
+            }
+
+
+def parse_genia_sentences(passage):
+    sentences = []
+    for sentence in passage.iter(tag="sentence"):
+        text = "".join(sentence.itertext())
+        entities = []
+        for entity in sentence.iter(tag="cons"):  # constituent
+            entity_lex = entity.get("lex", "")
+            entity_sem = parse_genia_sem(entity.get("sem", ""))
+            entity_text = "".join(entity.itertext())
+            entities.append({"text": entity_text, "lex": entity_lex, "sem": entity_sem})
+        sentences.append(
+            {
+                "text": text,
+                "entities": entities,
+            }
+        )
+    return sentences
+
+
+def parse_genia_bibliomisc(bibliomisc):
+    """Remove 'MEDLINE:' from 'MEDLINE:96055286'."""
+    return bibliomisc.replace("MEDLINE:", "") if ":" in bibliomisc else bibliomisc
+
+
+def parse_genia_sem(sem):
+    return sem.replace("G#", "") if "G#" in sem else sem
+
+
+def parse_genia_to_bigbio(example, uid):
+    document = {
+        "id": next(uid),
+        "document_id": example["document_id"],
+        "passages": list(generate_bigbio_passages(example, uid)),
+        "entities": list(generate_bigbio_entities(example, uid)),
+        "events": [],
+        "coreferences": [],
+        "relations": [],
+    }
+    return document
+
+
+def parse_genia_to_bigbio_passage(passage, uid, type="", offset=0):
+    text = " ".join(sentence["text"] for sentence in passage)
+    new_offset = offset + len(text)
+    return {
+        "id": next(uid),
+        "type": type,
+        "text": [text],
+        "offsets": [[offset, new_offset]],
+    }, new_offset + 1
+
+
+def generate_bigbio_passages(example, uid):
+    offset = 0
+    for type in ["title", "abstract"]:
+        passage, offset = parse_genia_to_bigbio_passage(
+            example[type], uid, type=type, offset=offset
+        )
+        yield passage
+
+
+def parse_genia_to_bigbio_entity(entity, uid, text="", relative_offset=0, offset=0):
+    try:
+        relative_offset = text.index(entity["text"], relative_offset)
+    except ValueError:
+        # Skip duplicated annotations:
+        # <cons lex="tumour_cell" sem="G#cell_type"><cons lex="tumour_cell" sem="G#cell_type">tumour cells</cons></cons>
+        return None, None
+    new_relative_offset = relative_offset + len(entity["text"])
+    return {
+        "id": next(uid),
+        "offsets": [[offset + relative_offset, offset + new_relative_offset]],
+        "text": [entity["text"]],
+        "type": entity["sem"],
+        "normalized": [],
+    }, new_relative_offset
+
+
+def generate_bigbio_entities(example, uid):
+    sentence_offset = 0
+    for type in ["title", "abstract"]:
+        for sentence in example[type]:
+            relative_offsets = {}
+            for entity in sentence["entities"]:
+                bigbio_entity, new_relative_offset = parse_genia_to_bigbio_entity(
+                    entity,
+                    uid,
+                    text=sentence["text"],
+                    relative_offset=relative_offsets.get(
+                        (entity["text"], entity["lex"], entity["sem"]), 0
+                    ),
+                    offset=sentence_offset,
+                )
+                if bigbio_entity:
+                    relative_offsets[
+                        (entity["text"], entity["lex"], entity["sem"])
+                    ] = new_relative_offset
+                    yield bigbio_entity
+            sentence_offset += len(sentence["text"]) + 1