|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" |
|
HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers |
|
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format, |
|
splitting each abstract into sentences, and in each sentence there may be entities and |
|
interactions between those entities. In this particular dataset, entities are all |
|
proteins and interactions are thus protein-protein interactions. |
|
|
|
Moreover, all entities are normalized to the HPRD database. These normalized terms are |
|
stored in each entity's 'type' attribute in the source XML. This means the dataset can |
|
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized |
|
entity. |
|
|
|
Because the dataset contains entities and relations, it is suitable for Named Entity |
|
Recognition and Relation Extraction. |
|
""" |
|
|
|
import os |
|
from glob import glob |
|
from typing import Dict, List, Tuple |
|
from xml.etree import ElementTree |
|
|
|
import datasets |
|
|
|
from .bigbiohub import kb_features |
|
from .bigbiohub import BigBioConfig |
|
from .bigbiohub import Tasks |
|
|
|
|
|
_LANGUAGES = ['English'] |
|
_PUBMED = True |
|
_LOCAL = False |
|
_CITATION = """\ |
|
@article{fundel2007relex, |
|
title={RelEx—Relation extraction using dependency parse trees}, |
|
author={Fundel, Katrin and K{\"u}ffner, Robert and Zimmer, Ralf}, |
|
journal={Bioinformatics}, |
|
volume={23}, |
|
number={3}, |
|
pages={365--371}, |
|
year={2007}, |
|
publisher={Oxford University Press} |
|
} |
|
""" |
|
|
|
_DATASETNAME = "hprd50" |
|
_DISPLAYNAME = "HPRD50" |
|
|
|
_DESCRIPTION = """\ |
|
HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers |
|
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format, |
|
splitting each abstract into sentences, and in each sentence there may be entities and |
|
interactions between those entities. In this particular dataset, entities are all |
|
proteins and interactions are thus protein-protein interactions. |
|
|
|
Moreover, all entities are normalized to the HPRD database. These normalized terms are |
|
stored in each entity's 'type' attribute in the source XML. This means the dataset can |
|
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized |
|
entity. |
|
|
|
Because the dataset contains entities and relations, it is suitable for Named Entity |
|
Recognition and Relation Extraction. |
|
""" |
|
|
|
_HOMEPAGE = "" |
|
|
|
_LICENSE = 'License information unavailable' |
|
|
|
_URLS = { |
|
_DATASETNAME: "https://github.com/metalrt/ppi-dataset/zipball/master", |
|
} |
|
|
|
_SUPPORTED_TASKS = [ |
|
Tasks.RELATION_EXTRACTION, |
|
Tasks.NAMED_ENTITY_RECOGNITION, |
|
] |
|
|
|
_SOURCE_VERSION = "1.0.0" |
|
|
|
_BIGBIO_VERSION = "1.0.0" |
|
|
|
|
|
def parse_xml_source(document_trees): |
|
entries = [] |
|
for doc in document_trees: |
|
document = { |
|
"id": doc.get("id"), |
|
"origId": doc.get("origId"), |
|
"set": doc.get("test"), |
|
"sentences": [], |
|
} |
|
for s in doc.findall("sentence"): |
|
sentence = { |
|
"id": s.get("id"), |
|
"origId": s.get("origId"), |
|
"charOffset": s.get("charOffset"), |
|
"text": s.get("text"), |
|
"entities": [], |
|
"interactions": [], |
|
} |
|
|
|
for e in s.findall("entity"): |
|
entity = { |
|
"id": e.get("id"), |
|
"origId": e.get("origId"), |
|
"charOffset": e.get("charOffset"), |
|
"text": e.get("text"), |
|
"type": e.get("type"), |
|
} |
|
|
|
sentence["entities"].append(entity) |
|
|
|
for i in s.findall("interaction"): |
|
interaction = { |
|
"id": i.get("id"), |
|
"e1": i.get("e1"), |
|
"e2": i.get("e2"), |
|
"type": i.get("type"), |
|
} |
|
sentence["interactions"].append(interaction) |
|
|
|
document["sentences"].append(sentence) |
|
|
|
entries.append(document) |
|
return entries |
|
|
|
|
|
def parse_xml_bigbio_kb(document_trees): |
|
entries = [] |
|
for doc in document_trees: |
|
document = { |
|
"id": doc.get("id"), |
|
"document_id": doc.get("origId"), |
|
"passages": [], |
|
"entities": [], |
|
"relations": [], |
|
"events": [], |
|
"coreferences": [], |
|
} |
|
for s in doc.findall("sentence"): |
|
|
|
offset = s.get("charOffset").split("-") |
|
start = int(offset[0]) |
|
end = int(offset[1]) |
|
|
|
passage = { |
|
"id": s.get("id"), |
|
"type": "sentence", |
|
"text": [s.get("text")], |
|
"offsets": [[start, end]], |
|
} |
|
|
|
document["passages"].append(passage) |
|
|
|
for e in s.findall("entity"): |
|
|
|
offset = e.get("charOffset").split("-") |
|
start = int(offset[0]) |
|
end = int(offset[1]) |
|
|
|
entity = { |
|
"id": e.get("id"), |
|
"text": [e.get("text")], |
|
"offsets": [[start, end]], |
|
"type": "protein", |
|
"normalized": [{"db_name": "HPRD", "db_id": e.get("type")}], |
|
} |
|
|
|
document["entities"].append(entity) |
|
|
|
for i in s.findall("interaction"): |
|
relation = { |
|
"id": i.get("id"), |
|
"arg1_id": i.get("e1"), |
|
"arg2_id": i.get("e2"), |
|
"type": i.get("type"), |
|
"normalized": [], |
|
} |
|
document["relations"].append(relation) |
|
|
|
entries.append(document) |
|
return entries |
|
|
|
|
|
class HPRD50Dataset(datasets.GeneratorBasedBuilder): |
|
""" |
|
HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers |
|
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format, |
|
splitting each abstract into sentences, and in each sentence there may be entities and |
|
interactions between those entities. In this particular dataset, entities are all |
|
proteins and interactions are thus protein-protein interactions. |
|
|
|
Moreover, all entities are normalized to the HPRD database. These normalized terms are |
|
stored in each entity's 'type' attribute in the source XML. This means the dataset can |
|
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized |
|
entity. |
|
|
|
Because the dataset contains entities and relations, it is suitable for Named Entity |
|
Recognition and Relation Extraction. |
|
""" |
|
|
|
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
|
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
|
|
|
BUILDER_CONFIGS = [ |
|
BigBioConfig( |
|
name="hprd50_source", |
|
version=SOURCE_VERSION, |
|
description="hprd50 source schema", |
|
schema="source", |
|
subset_id="hprd50", |
|
), |
|
BigBioConfig( |
|
name="hprd50_bigbio_kb", |
|
version=BIGBIO_VERSION, |
|
description="hprd50 BigBio schema", |
|
schema="bigbio_kb", |
|
subset_id="hprd50", |
|
), |
|
] |
|
|
|
DEFAULT_CONFIG_NAME = "hprd50_source" |
|
|
|
def _info(self) -> datasets.DatasetInfo: |
|
|
|
if self.config.schema == "source": |
|
features = datasets.Features( |
|
{ |
|
"id": datasets.Value("string"), |
|
"origId": datasets.Value("string"), |
|
"set": datasets.Value("string"), |
|
"sentences": [ |
|
{ |
|
"id": datasets.Value("string"), |
|
"origId": datasets.Value("string"), |
|
"charOffset": datasets.Value("string"), |
|
"text": datasets.Value("string"), |
|
"entities": [ |
|
{ |
|
"id": datasets.Value("string"), |
|
"origId": datasets.Value("string"), |
|
"charOffset": datasets.Value("string"), |
|
"text": datasets.Value("string"), |
|
"type": datasets.Value("string"), |
|
} |
|
], |
|
"interactions": [ |
|
{ |
|
"id": datasets.Value("string"), |
|
"e1": datasets.Value("string"), |
|
"e2": datasets.Value("string"), |
|
"type": 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) |
|
|
|
|
|
data_dir = glob(f"{data_dir}/**/csv_output")[0] |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
|
|
gen_kwargs={ |
|
"filepath": os.path.join(data_dir, "HPRD50-train.xml"), |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": os.path.join(data_dir, "HPRD50-test.xml"), |
|
"split": "test", |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: |
|
"""Yields examples as (key, example) tuples.""" |
|
|
|
with open(filepath, "r") as f: |
|
content = f.read() |
|
|
|
tree = ElementTree.fromstring(content) |
|
documents = tree.findall("document") |
|
|
|
if self.config.schema == "source": |
|
entries = parse_xml_source(documents) |
|
for key, example in enumerate(entries): |
|
yield key, example |
|
|
|
elif self.config.schema == "bigbio_kb": |
|
entries = parse_xml_bigbio_kb(documents) |
|
for key, example in enumerate(entries): |
|
yield key, example |
|
|
|
|
|
|
|
|
|
|