upload hubscripts/msh_wsd_hub.py to hub from bigbio repo

msh_wsd.py

@@ -0,0 +1,268 @@
+# 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.
+
+"""
+Evaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available
+resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have
+developed a method that can be used to automatically develop a WSD test collection using the Unified Medical Language
+System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and
+consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203
+ambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS.
+For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from
+MEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations.
+
+Note from the Author how to load dataset:
+1) Download the file MSHCorpus.zip (Link "MSHWSD Data Set") from
+   https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html
+2) Set kwarg data_dir to the directory containing MSHCorpus.zip
+"""
+
+import itertools as it
+import os
+import re
+from dataclasses import dataclass
+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 = True
+_CITATION = """\
+@article{jimeno2011exploiting,
+  title={Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation},
+  author={Jimeno-Yepes, Antonio J and McInnes, Bridget T and Aronson, Alan R},
+  journal={BMC bioinformatics},
+  volume={12},
+  number={1},
+  pages={1--14},
+  year={2011},
+  publisher={BioMed Central}
+}
+"""
+
+_DESCRIPTION = """\
+Evaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available
+resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have
+developed a method that can be used to automatically develop a WSD test collection using the Unified Medical Language
+System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and
+consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203
+ambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS.
+For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from
+MEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations.
+"""
+
+_DATASETNAME = "msh_wsd"
+_DISPLAYNAME = "MSH WSD"
+
+_HOMEPAGE = "https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html"
+
+_LICENSE = 'UMLS - Metathesaurus License Agreement'
+
+_URLS = {_DATASETNAME: ""}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_DISAMBIGUATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+@dataclass
+class MshWsdBigBioConfig(BigBioConfig):
+    schema: str = "source"
+    name: str = "msh_wsd_source"
+    version: datasets.Version = datasets.Version(_SOURCE_VERSION)
+    description: str = "MSH-WSD source schema"
+    subset_id: str = "msh_wsd"
+
+
+class MshWsdDataset(datasets.GeneratorBasedBuilder):
+    """Biomedical Word Sense Disambiguation (WSD)."""
+
+    uid = it.count(0)
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        MshWsdBigBioConfig(
+            name="msh_wsd_source",
+            version=SOURCE_VERSION,
+            description="MSH-WSD source schema",
+            schema="source",
+            subset_id="msh_wsd",
+        ),
+        MshWsdBigBioConfig(
+            name="msh_wsd_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="MSH-WSD BigBio schema",
+            schema="bigbio_kb",
+            subset_id="msh_wsd",
+        ),
+    ]
+
+    BUILDER_CONFIG_CLASS = MshWsdBigBioConfig
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "ambiguous_word": datasets.Value("string"),
+                    "sentences": [
+                        {
+                            "pmid": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "label": datasets.Value("string"),
+                        }
+                    ],
+                    "choices": [
+                        {
+                            "label": datasets.Value("string"),
+                            "concept": 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."""
+
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+        else:
+            data_dir = dl_manager.download_and_extract(
+                os.path.join(self.config.data_dir, "MSHCorpus.zip")
+            )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": Path(data_dir),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data_dir = data_dir / "MSHCorpus"
+        concepts = data_dir / "benchmark_mesh.txt"
+        with concepts.open() as f:
+            concepts = f.readlines()
+        concepts = [x.strip().split("\t") for x in concepts]
+
+        concept_map = {
+            cuis[0]: {f"M{num}": cui for num, cui in enumerate(cuis[1:], 1)}
+            for cuis in concepts
+        }
+
+        files = list(data_dir.glob("*arff"))
+        for guid, file in enumerate(files):
+            if self.config.schema == "source":
+                for example in self._parse_document(concept_map, file):
+                    yield guid, example
+
+            elif self.config.schema == "bigbio_kb":
+                for document in self._parse_document(concept_map, file):
+                    for example in self._source_to_kb(document):
+                        yield example["id"], example
+
+    def _parse_document(self, concept_map, file: Path):
+        with file.open(mode="r", encoding="iso-8859-1") as f:
+            content = f.readlines()
+        content = [x.strip() for x in content]
+
+        # search line number of @DATA, sometimes 6 or 7
+        start_l = None
+        for number, line in enumerate(content):
+            if line.startswith("@DATA"):
+                start_l = number + 1
+                break
+        assert start_l is not None
+
+        amb_word = file.with_suffix("").name[: -len("_pmids_tagged")]
+
+        sentences = []
+        for line in content[start_l:]:
+            # cant use , or ," ", as seperator
+            m_pmid = re.search("[0-9]+(?=(,))", line)
+            pmid = m_pmid.group()
+            m_label = re.search("(?<=(,))M[0-9]+", line)
+            label = m_label.group()
+
+            citation = line[m_pmid.span()[1] + 1 : m_label.span()[0] - 1].strip('"')
+
+            sentences.append({"pmid": pmid, "text": citation, "label": label})
+
+        yield {
+            "ambiguous_word": amb_word,
+            "sentences": sentences,
+            "choices": [
+                {"label": key, "concept": value}
+                for key, value in concept_map[amb_word].items()
+            ],
+        }
+
+    def _source_to_kb(self, document):
+        choices = {x["label"]: x["concept"] for x in document["choices"]}
+        for sentence in document["sentences"]:
+            document_ = {}
+            document_["events"] = []
+            document_["relations"] = []
+            document_["coreferences"] = []
+            document_["id"] = next(self.uid)
+            document_["document_id"] = sentence["pmid"]
+            document_["passages"] = [
+                {
+                    "id": next(self.uid),
+                    "type": "",
+                    "text": [sentence["text"]],
+                    "offsets": [[0, len(sentence["text"])]],
+                }
+            ]
+            document_["entities"] = [
+                {
+                    "id": next(self.uid),
+                    "type": "ambiguous_word",
+                    "text": [document["ambiguous_word"]],
+                    "offsets": [self._parse_offset(sentence["text"])],
+                    "normalized": [
+                        {"db_name": "MeSH", "db_id": choices[sentence["label"]]}
+                    ],
+                }
+            ]
+            yield document_
+
+    def _parse_offset(self, sentence):
+        m = re.search("(?<=(<e>)).+(?=(</e>))", sentence)
+        return m.span()