# Copyright 2020 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.
"""Semeval2018Task7 is a dataset that describes the first task on semantic relation extraction and classification in scientific paper abstracts"""
import glob
import datasets
import xml.dom.minidom
import xml.etree.ElementTree as ET
_CITATION = """\
@inproceedings{gabor-etal-2018-semeval,
title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
author = {G{\'a}bor, Kata and
Buscaldi, Davide and
Schumann, Anne-Kathrin and
QasemiZadeh, Behrang and
Zargayouna, Ha{\"\i}fa and
Charnois, Thierry},
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1111",
doi = "10.18653/v1/S18-1111",
pages = "679--688",
abstract = "This paper describes the first task on semantic relation extraction and classification in
scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations
and includes three different subtasks. The subtasks were designed so as to compare and quantify the
effect of different pre-processing steps on the relation classification results. We expect the task to
be relevant for a broad range of researchers working on extracting specialized knowledge from domain
corpora, for example but not limited to scientific or bio-medical information extraction. The task
attracted a total of 32 participants, with 158 submissions across different scenarios.",
}
"""
_DESCRIPTION = """\
This paper describes the first task on semantic relation extraction and classification in scientific paper
abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
different subtasks. The subtasks were designed so as to compare and quantify the effect of different
pre-processing steps on the relation classification results. We expect the task to be relevant for a broad
range of researchers working on extracting specialized knowledge from domain corpora, for example but not
limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants,
with 158 submissions across different scenarios.
"""
_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
_LICENSE = ""
# Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"Subtask_1_1": {
"train": {
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.relations.txt",
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.text.xml",
},
"test": {
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.relations.txt",
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.text.xml",
},
},
"Subtask_1_2": {
"train": {
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.relations.txt",
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.text.xml",
},
"test": {
"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.relations.txt",
"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.text.xml",
},
},
}
def all_text_nodes(root):
if root.text is not None:
yield root.text
for child in root:
if child.tail is not None:
yield child.tail
def reading_entity_data(ET_data_to_convert):
parsed_data = ET.tostring(ET_data_to_convert,"utf-8")
parsed_data= parsed_data.decode('utf8').replace("b\'","")
parsed_data= parsed_data.replace("","")
parsed_data= parsed_data.replace("","")
parsed_data= parsed_data.replace("","")
parsed_data= parsed_data.replace("","")
parsed_data = parsed_data.replace("\n\n\n","")
parsing_tag = False
final_string = ""
tag_string= ""
current_tag_id = ""
current_tag_starting_pos = 0
current_tag_ending_pos= 0
entity_mapping_list=[]
for i in parsed_data:
if i=='<':
parsing_tag = True
if current_tag_id!="":
current_tag_ending_pos = len(final_string)-1
entity_mapping_list.append({"id":current_tag_id,
"char_start":current_tag_starting_pos,
"char_end":current_tag_ending_pos+1})
current_tag_id= ""
tag_string=""
elif i=='>':
parsing_tag = False
tag_string_split = tag_string.split('"')
if len(tag_string_split)>1:
current_tag_id= tag_string.split('"')[1]
current_tag_starting_pos = len(final_string)
else:
if parsing_tag!=True:
final_string = final_string + i
else:
tag_string = tag_string + i
return {"text_data":final_string, "entities":entity_mapping_list}
class Semeval2018Task7(datasets.GeneratorBasedBuilder):
"""
Semeval2018Task7 is a dataset for semantic relation extraction and classification in scientific paper abstracts
"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="Subtask_1_1", version=VERSION,
description="Relation classification on clean data"),
datasets.BuilderConfig(name="Subtask_1_2", version=VERSION,
description="Relation classification on noisy data"),
]
DEFAULT_CONFIG_NAME = "Subtask_1_1"
def _info(self):
class_labels = ["","USAGE", "RESULT", "MODEL-FEATURE", "PART_WHOLE", "TOPIC", "COMPARE"]
features = datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"abstract": datasets.Value("string"),
"entities": [
{
"id": datasets.Value("string"),
"char_start": datasets.Value("int32"),
"char_end": datasets.Value("int32")
}
],
"relations": [
{
"label": datasets.ClassLabel(names=class_labels),
"arg1": datasets.Value("string"),
"arg2": datasets.Value("string"),
"reverse": datasets.Value("bool")
}
]
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
urls = _URLS[self.config.name]
downloaded_files = dl_manager.download(urls)
print(downloaded_files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"relation_filepath": downloaded_files['train']["relations"],
"text_filepath": downloaded_files['train']["text"],
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"relation_filepath": downloaded_files['test']["relations"],
"text_filepath": downloaded_files['test']["text"],
}
)]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, relation_filepath, text_filepath):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(relation_filepath, encoding="utf-8") as f:
relations = []
text_id_to_relations_map= {}
for key, row in enumerate(f):
row_split = row.strip("\n").split("(")
use_case = row_split[0]
second_half = row_split[1].strip(")")
second_half_splits = second_half.split(",")
size = len(second_half_splits)
relation = {
"label": use_case,
"arg1": second_half_splits[0],
"arg2": second_half_splits[1],
"reverse": True if size == 3 else False
}
relations.append(relation)
arg_id = second_half_splits[0].split(".")[0]
if arg_id not in text_id_to_relations_map:
text_id_to_relations_map[arg_id] = [relation]
else:
text_id_to_relations_map[arg_id].append(relation)
doc2 = ET.parse(text_filepath)
root = doc2.getroot()
for child in root:
if child.find("title")==None:
continue
text_id = child.attrib
if child.find("abstract")==None:
continue
title = child.find("title").text
child_abstract = child.find("abstract")
abstract_text_and_entities = reading_entity_data(child.find("abstract"))
title_text_and_entities = reading_entity_data(child.find("title"))
text_relations = []
if text_id['id'] in text_id_to_relations_map:
text_relations = text_id_to_relations_map[text_id['id']]
yield text_id['id'], {
"id": text_id['id'],
"title": title_text_and_entities['text_data'],
"abstract": abstract_text_and_entities['text_data'],
"entities": abstract_text_and_entities['entities'] + title_text_and_entities['entities'],
"relations": text_relations
}